Sawyer Benson’s Master Thesis

Janurary 10, 2022

1. Model Design: Checks & Corrections

1.1 Accounting for Heteroskedasticity

summ(lm_pre_alpha)
MODEL INFO:
Observations: 24394 (18 missing obs. deleted)
Dependent Variable: sold_price
Type: OLS linear regression 

MODEL FIT:
F(65,24328) = 37208.82, p = 0.00
R² = 0.99
Adj. R² = 0.99 

Standard errors: OLS
-------------------------------------------------------------------------
                                           Est.      S.E.   t val.      p
----------------------------------- ----------- --------- -------- ------
(Intercept)                           -16471.89   9512.56    -1.73   0.08
property_typeDUP                       -1567.54   2875.46    -0.55   0.59
property_typeOTH                       -2779.57   2056.27    -1.35   0.18
property_typePAT                        -565.21    930.77    -0.61   0.54
property_typeSGL                        1807.60    438.32     4.12   0.00
property_typeTNH                         529.68    552.32     0.96   0.34
ac_typenone                              -55.39    381.27    -0.15   0.88
ac_typenot_central                     -1637.94    246.08    -6.66   0.00
list_price                                 0.98      0.00   895.29   0.00
patio1                                   833.04    126.89     6.57   0.00
school_general1                          224.32    161.80     1.39   0.17
photo_count                              -34.93      7.63    -4.58   0.00
pool1                                   -157.09    211.72    -0.74   0.46
roof_typeother                          1179.35    233.09     5.06   0.00
roof_typeshingle                        1981.04    262.17     7.56   0.00
roof_typeslate                           561.38   1115.28     0.50   0.61
gas_typenatural                         4523.85   8545.09     0.53   0.60
gas_typenone                            3936.67   8541.01     0.46   0.64
gas_typepropane                          -96.15   8741.58    -0.01   0.99
gas_typeunknown                         3661.78   8540.06     0.43   0.67
out_building1                           -490.46    137.74    -3.56   0.00
area_living                               -0.81      0.27    -2.97   0.00
land_acres                              -291.00    154.61    -1.88   0.06
appliances1                              928.60    172.69     5.38   0.00
garage1                                  700.15    126.88     5.52   0.00
property_conditionnew                  -3450.47    785.55    -4.39   0.00
property_conditionother                 -354.50    169.05    -2.10   0.04
energy_efficient1                        592.05    141.81     4.18   0.00
exterior_typemetal                       -44.83    402.78    -0.11   0.91
exterior_typeother                        54.80    167.73     0.33   0.74
exterior_typevinyl                       410.53    186.16     2.21   0.03
exterior_typewood                       -611.49    263.14    -2.32   0.02
exterior_featurescourtyard              2804.70   1467.81     1.91   0.06
exterior_featuresfence                  1047.68    615.20     1.70   0.09
exterior_featuresnone                   1600.42    616.39     2.60   0.01
exterior_featuresporch                  1133.90    629.71     1.80   0.07
exterior_featurestennis_court            718.57   1727.07     0.42   0.68
fireplace1                               329.21    131.35     2.51   0.01
foundation_typeslab                      813.97    190.06     4.28   0.00
foundation_typeunspecified              -244.66    228.76    -1.07   0.28
area_total                                -0.20      0.16    -1.26   0.21
beds_total1                             -654.41   3179.87    -0.21   0.84
beds_total2                            -1146.38   3149.18    -0.36   0.72
beds_total3                             -501.07   3152.57    -0.16   0.87
beds_total4                              352.34   3158.67     0.11   0.91
beds_total5                             -459.18   3216.99    -0.14   0.89
bath_full1                              2422.94   3359.73     0.72   0.47
bath_full2                              2893.97   3359.50     0.86   0.39
bath_full3                              2434.57   3367.54     0.72   0.47
bath_full4                             -2224.48   3760.16    -0.59   0.55
bath_full6                             -3834.27   9210.17    -0.42   0.68
bath_half1                              -365.47    166.84    -2.19   0.03
bath_half2                             -1666.56   1100.38    -1.51   0.13
bath_half3                              1695.30   6037.98     0.28   0.78
bath_half4                              8512.66   8544.34     1.00   0.32
bath_half5                             -8585.74   4939.03    -1.74   0.08
age                                      -37.65      3.75   -10.03   0.00
dom                                       -8.30      1.08    -7.68   0.00
sold_date                                  0.29      0.06     4.58   0.00
sewer_typeseptic                        -291.81    237.10    -1.23   0.22
sewer_typeunspecified                    268.03    129.53     2.07   0.04
property_stylenot_mobile                2238.58    353.84     6.33   0.00
subdivision1                             392.59    151.73     2.59   0.01
water_typewell                           550.97    600.38     0.92   0.36
waterfront1                            -1629.56    225.69    -7.22   0.00
bottom25_dom1                           2327.32    159.03    14.63   0.00
-------------------------------------------------------------------------


1.2 Accounting for Interactions

Note: Advisor suggested not to inlude interaction terms except for specific testing.


1.3 Accounting for Non-linearity

1.3.1 Age
a
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
<<<<<<< HEAD

=======

>>>>>>> cd3d4497875c198536d12af185cf61114c92970a
gridExtra::grid.arrange(b,c, nrow =2, ncol = 1)
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
<<<<<<< HEAD

=======

>>>>>>> cd3d4497875c198536d12af185cf61114c92970a


1.3.2 Living Area
# Living Area

# General graphing
a <- ggplot(data_factor, aes(x = area_living , y = sold_price)) + 
    geom_point(aes(color = infections_period), alpha = 0.15) + 
    geom_smooth(aes(color = infections_period)) +
    geom_smooth(color = "grey50", linetype = "dashed") +
    theme_minimal()

ggplot(data_factor, aes(x = area_living , y = sold_price/area_living)) + 
    geom_point(aes(color = infections_period), alpha = 0.15) + 
    geom_smooth(aes(color = infections_period)) +
    geom_smooth(color = "grey50", linetype = "dashed") +
    theme_minimal()
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

# Actual vs. fit
# Model with non-linear addition
lm_pre_alpha_area <- lm(sold_price ~ . + I(area_living^2), data = data_factor_core)
summ(lm_pre_alpha_area)
MODEL INFO:
Observations: 24394 (18 missing obs. deleted)
Dependent Variable: sold_price
Type: OLS linear regression 

MODEL FIT:
F(66,24327) = 36741.79, p = 0.00
R² = 0.99
Adj. R² = 0.99 

Standard errors: OLS
-------------------------------------------------------------------------
                                           Est.      S.E.   t val.      p
----------------------------------- ----------- --------- -------- ------
(Intercept)                           -21671.88   9522.17    -2.28   0.02
property_typeDUP                       -1333.48   2871.86    -0.46   0.64
property_typeOTH                       -2804.77   2053.59    -1.37   0.17
property_typePAT                        -620.44    929.59    -0.67   0.50
property_typeSGL                        1770.31    437.77     4.04   0.00
property_typeTNH                         370.43    551.95     0.67   0.50
ac_typenone                               62.25    381.06     0.16   0.87
ac_typenot_central                     -1498.05    246.37    -6.08   0.00
list_price                                 0.98      0.00   896.13   0.00
patio1                                   798.99    126.79     6.30   0.00
school_general1                          241.58    161.60     1.49   0.13
photo_count                              -34.70      7.62    -4.55   0.00
pool1                                    -73.45    211.70    -0.35   0.73
roof_typeother                          1098.57    233.01     4.71   0.00
roof_typeshingle                        1920.08    261.94     7.33   0.00
roof_typeslate                           536.02   1113.83     0.48   0.63
gas_typenatural                         4855.78   8534.04     0.57   0.57
gas_typenone                            4318.56   8530.00     0.51   0.61
gas_typepropane                           87.56   8730.21     0.01   0.99
gas_typeunknown                         3979.77   8529.01     0.47   0.64
out_building1                           -490.59    137.56    -3.57   0.00
area_living                                6.54      0.95     6.85   0.00
land_acres                              -285.71    154.41    -1.85   0.06
appliances1                              921.60    172.47     5.34   0.00
garage1                                  666.84    126.78     5.26   0.00
property_conditionnew                  -3617.20    784.80    -4.61   0.00
property_conditionother                 -364.93    168.83    -2.16   0.03
energy_efficient1                        601.45    141.63     4.25   0.00
exterior_typemetal                        16.32    402.32     0.04   0.97
exterior_typeother                        58.29    167.52     0.35   0.73
exterior_typevinyl                       417.26    185.92     2.24   0.02
exterior_typewood                       -554.23    262.89    -2.11   0.04
exterior_featurescourtyard              2805.14   1465.90     1.91   0.06
exterior_featuresfence                  1048.09    614.40     1.71   0.09
exterior_featuresnone                   1584.20    615.59     2.57   0.01
exterior_featuresporch                  1119.15    628.89     1.78   0.08
exterior_featurestennis_court            870.69   1724.92     0.50   0.61
fireplace1                               264.36    131.42     2.01   0.04
foundation_typeslab                      819.18    189.82     4.32   0.00
foundation_typeunspecified              -213.55    228.49    -0.93   0.35
area_total                                -0.27      0.16    -1.71   0.09
beds_total1                            -1072.82   3176.15    -0.34   0.74
beds_total2                            -2553.13   3149.94    -0.81   0.42
beds_total3                            -2327.60   3156.66    -0.74   0.46
beds_total4                            -1389.50   3161.99    -0.44   0.66
beds_total5                            -1954.03   3218.17    -0.61   0.54
bath_full1                              3642.81   3358.78     1.08   0.28
bath_full2                              3719.16   3356.69     1.11   0.27
bath_full3                              3740.67   3367.07     1.11   0.27
bath_full4                              -544.43   3761.07    -0.14   0.88
bath_full6                             -3367.38   9198.35    -0.37   0.71
bath_half1                              -274.20    167.01    -1.64   0.10
bath_half2                             -1480.96   1099.19    -1.35   0.18
bath_half3                              1451.22   6030.19     0.24   0.81
bath_half4                              7762.17   8533.71     0.91   0.36
bath_half5                             -8041.27   4933.05    -1.63   0.10
age                                      -37.00      3.75    -9.87   0.00
dom                                       -8.28      1.08    -7.66   0.00
sold_date                                  0.28      0.06     4.35   0.00
sewer_typeseptic                        -304.75    236.80    -1.29   0.20
sewer_typeunspecified                    258.97    129.37     2.00   0.05
property_stylenot_mobile                2105.89    353.77     5.95   0.00
subdivision1                             401.26    151.53     2.65   0.01
water_typewell                           557.86    599.60     0.93   0.35
waterfront1                            -1642.44    225.40    -7.29   0.00
bottom25_dom1                           2331.25    158.82    14.68   0.00
I(area_living^2)                          -0.00      0.00    -8.04   0.00
-------------------------------------------------------------------------
# Model with single-variable fit
lm_pre_alpha_area_single <- lm(sold_price ~ area_living, data = data_factor_core)
summ(lm_pre_alpha_area_single)
MODEL INFO:
Observations: 24412
Dependent Variable: sold_price
Type: OLS linear regression 

MODEL FIT:
F(1,24410) = 14244.19, p = 0.00
R² = 0.37
Adj. R² = 0.37 

Standard errors: OLS
-------------------------------------------------------
                         Est.      S.E.   t val.      p
----------------- ----------- --------- -------- ------
(Intercept)         -20238.66   1644.55   -12.31   0.00
area_living            113.16      0.95   119.35   0.00
-------------------------------------------------------
# Marginal effects data frames
ggpredict_1 <- ggpredict(lm_pre_alpha, terms = "area_living") # total model
ggpredict_2 <- ggpredict(lm_pre_alpha_area, terms = "area_living") # non-linear addition
ggpredict_3 <- ggpredict(lm_pre_alpha_area_single, terms = "area_living") # single-variable fit

# Plots
b <- ggplot(data_factor_core, aes(x = area_living)) +
   geom_smooth(data_factor, mapping = aes(y = sold_price), color = "grey50") +
   geom_smooth(ggpredict_1, mapping = aes(x, predicted), linetype = "dashed", color = "darkred") +
   geom_smooth(ggpredict_2, mapping = aes(x, predicted), linetype = "dashed", color = "darkblue") +
   geom_smooth(ggpredict_3, mapping = aes(x, predicted), linetype = "dashed", color = "darkblue")

# Look at age & age^2 alone to see impact on more relevant y-axis scale
c <- ggplot() +
   geom_smooth(ggpredict_1, mapping = aes(x, predicted), linetype = "dashed", color = "darkred") +
   geom_smooth(ggpredict_2, mapping = aes(x, predicted), linetype = "dashed", color = "darkblue") 

# Conclusion
a
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

gridExtra::grid.arrange(b,c, nrow =2, ncol = 1)
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'


1.3.3 Land
# General graphing
ggplot(data_factor, aes(x = land_acres , y = sold_price)) + 
    geom_point(aes(color = infections_period), alpha = 0.15) + 
    geom_smooth(aes(color = infections_period)) +
    geom_smooth(color = "grey50", linetype = "dashed") +
    theme_minimal()
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

ggplot(data_factor, aes(x = land_acres, y = sold_price/land_acres)) + 
    geom_point(aes(color = infections_period), alpha = 0.15) + 
    geom_smooth(aes(color = infections_period)) +
    geom_smooth(color = "grey50", linetype = "dashed") +
    theme_minimal()
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'


1.3.4 Non-linear Additions
#Additions
data_factor_core_clean <- data_factor_core
data_factor_core_clean$age_2 <- I(data_factor_core$age^2)
data_factor_core_clean$area_living_2 <- I(data_factor_core$area_living^2)


1.4 Accounting for Multicollinearity

<<<<<<< HEAD

=======

>>>>>>> cd3d4497875c198536d12af185cf61114c92970a
a
b

c
<<<<<<< HEAD

=======

>>>>>>> cd3d4497875c198536d12af185cf61114c92970a


1.4.1 Multicollinearity Removals
# Removals
# - Area_total
# - Listing price

data_factor_core_clean <- subset(data_factor_core_clean, select = -c(area_total, list_price))


1.5 High-leverage Removals

data_factor_core_clean <- data_factor_core_clean[-c(23515)]
Error: Can't negate columns that don't exist.
x Location 23515 doesn't exist.
ℹ There are only 34 columns.
Backtrace:
 1. data_factor_core_clean[-c(23515)]
 2. tibble:::`[.tbl_df`(data_factor_core_clean, -c(23515))
 3. tibble:::vectbl_as_col_location(...)
 6. vctrs::vec_as_location(j, n, names)
 8. vctrs:::stop_subscript_oob(...)
 9. vctrs:::stop_subscript(...)


1.5 Alpha Model

summ(lm_alpha)
MODEL INFO:
Observations: 24393 (18 missing obs. deleted)
Dependent Variable: sold_price
Type: Linear regression 

MODEL FIT:
χ²(64) = 117172996786096.98, p = 0.00
Pseudo-R² (Cragg-Uhler) = 0.66
Pseudo-R² (McFadden) = 0.04
AIC = 597055.98, BIC = 597590.71 

Standard errors: MLE
--------------------------------------------------------------------------
                                           Est.       S.E.   t val.      p
----------------------------------- ----------- ---------- -------- ------
(Intercept)                             7944.53   25040.74     0.32   0.75
property_typeDUP                      -49776.83   16818.97    -2.96   0.00
property_typeOTH                       16195.71   12028.70     1.35   0.18
property_typePAT                       15014.63    5441.44     2.76   0.01
property_typeSGL                       21550.02    2545.76     8.47   0.00
property_typeTNH                       -3912.40    3231.68    -1.21   0.23
ac_typenone                           -44471.24    2213.16   -20.09   0.00
ac_typenot_central                    -14312.82    1441.33    -9.93   0.00
patio1                                  8218.69     739.05    11.12   0.00
school_general1                        12474.85     944.70    13.21   0.00
photo_count                              903.06      44.37    20.35   0.00
pool1                                  11856.09    1238.46     9.57   0.00
roof_typeother                          3337.24    1359.27     2.46   0.01
roof_typeshingle                       20790.72    1527.80    13.61   0.00
roof_typeslate                          6522.07    6525.08     1.00   0.32
gas_typenone                          -33136.30    2147.25   -15.43   0.00
gas_typepropane                        -3375.37   11132.52    -0.30   0.76
gas_typeunknown                       -38807.07    2107.17   -18.42   0.00
out_building1                          -5970.10     800.48    -7.46   0.00
area_living                               42.15       5.43     7.76   0.00
land_acres                              2513.39     898.79     2.80   0.01
appliances1                            25181.42     999.29    25.20   0.00
garage1                                12206.68     733.84    16.63   0.00
property_conditionnew                 -25781.04    4623.44    -5.58   0.00
property_conditionother               -21654.41     977.66   -22.15   0.00
energy_efficient1                      14013.73     823.53    17.02   0.00
exterior_typemetal                      -478.40    2356.87    -0.20   0.84
exterior_typeother                     10423.23     979.05    10.65   0.00
exterior_typevinyl                      4548.23    1088.81     4.18   0.00
exterior_typewood                       3158.59    1539.38     2.05   0.04
exterior_featurescourtyard             36564.44    8587.97     4.26   0.00
exterior_featuresfence                -25346.57    3594.93    -7.05   0.00
exterior_featuresnone                 -18900.07    3603.69    -5.24   0.00
exterior_featuresporch                -25801.74    3682.07    -7.01   0.00
exterior_featurestennis_court          10302.14   10104.95     1.02   0.31
fireplace1                             11754.15     768.42    15.30   0.00
foundation_typeslab                    15254.06    1116.34    13.66   0.00
foundation_typeunspecified              8258.34    1340.03     6.16   0.00
beds_total1                           -32233.49   18604.90    -1.73   0.08
beds_total2                           -44404.19   18451.56    -2.41   0.02
beds_total3                           -50134.64   18490.29    -2.71   0.01
beds_total4                           -46519.73   18521.48    -2.51   0.01
beds_total5                           -61554.10   18848.52    -3.27   0.00
bath_full1                            -30516.77   19675.99    -1.55   0.12
bath_full2                             -6674.87   19664.68    -0.34   0.73
bath_full3                             16217.61   19725.77     0.82   0.41
bath_full4                             12634.06   22034.17     0.57   0.57
bath_full6                             18673.99   53893.54     0.35   0.73
bath_half1                             12465.57     974.62    12.79   0.00
bath_half2                             30380.88    6429.41     4.73   0.00
bath_half3                             56559.91   35321.60     1.60   0.11
bath_half4                             91519.65   49985.88     1.83   0.07
bath_half5                            -56904.41   28892.95    -1.97   0.05
age                                    -1886.46      63.76   -29.59   0.00
dom                                      -20.28       6.33    -3.20   0.00
sold_date                                  4.26       0.38    11.15   0.00
sewer_typeseptic                       -6477.81    1388.39    -4.67   0.00
sewer_typeunspecified                  -4734.60     756.49    -6.26   0.00
property_stylenot_mobile               68303.77    2024.52    33.74   0.00
subdivision1                            3626.89     887.21     4.09   0.00
water_typewell                          2051.36    3510.88     0.58   0.56
waterfront1                            19813.22    1313.09    15.09   0.00
bottom25_dom1                          11493.99     928.88    12.37   0.00
age_2                                     16.76       0.81    20.76   0.00
area_living_2                              0.01       0.00     3.78   0.00
--------------------------------------------------------------------------

Estimated dispersion parameter = 2490897637 


2. Factor Analysis

2.1 Corona
2.1.1 Visualization

# Waves of infection
ggplot(data_factor, aes(x = as.Date(sold_date), y = infections_3mma)) + 
    geom_point(color = "grey35") + 
    geom_smooth(linetype = "dashed", color = "gray46") +
    theme_minimal() +
    scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    scale_y_continuous(limits = c(0,max(infections_3mma))) +
    xlab(" ") +
    ylab("Confirmed Infections per Day") +
    labs(title = "Waves of Infection",
         caption = "") +
    geom_vline(xintercept = as.numeric(as.Date("2020-03-23")), linetype=4)
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Warning: Removed 17731 rows containing non-finite values (stat_smooth).
Warning: Removed 17731 rows containing missing values (geom_point).
Warning: Removed 3 rows containing missing values (geom_smooth).

# Accumulation of infections
ggplot(data_factor, aes(x = as.Date(sold_date), y = I(infections_accum/1000))) + 
    geom_point(color = "grey35") + 
    geom_smooth(linetype = "dashed", color = "gray46") +
    theme_minimal() +
    scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    scale_y_continuous(limits = c(0,max(I(infections_accum/1000)))) +
    xlab(" ") +
    ylab("Accumulation of Infections (in 000's") +
    labs(title = "Accumulation of Infections",
         caption = "")
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Warning: Removed 17731 rows containing non-finite values (stat_smooth).
Warning: Removed 17731 rows containing missing values (geom_point).
Warning: Removed 3 rows containing missing values (geom_smooth).

# Infections and home prices
ggplot(data_factor, aes(x = I(infections_3mma/1000), y = sold_price)) + 
    #geom_point() + 
    geom_smooth(linetype = "dashed", color = "gray46") +
    theme_minimal() +
    scale_x_continuous( limits = c(0,max(I(infections_3mma/1000)))) +
    xlab("3-Month Moving Average of Daily Infections (in 000's)") +
    ylab("Sold Price (Actual)") +
    labs(title = "Infections and Price",
         caption = "")
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

#Price on Infections
very_low <- "#460f5c"
low <- "#2c728e"
med <- "#27ad81"
high <- "#f4e61e"

# "#ff6c67", "#00c2c6"

ggplot(data_factor, aes(x = infections_period, y = sold_price, fill = infections_period)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)) +
    ggtitle("Comparison of Sold Price") +
    xlab("Infections Present (1 = yes)") +
    scale_fill_manual(values=c(very_low, med))
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.



2.1.2 Modeling
# Testing Corona
lm_corona <- lm(sold_price ~ infections_3mma + . 
                
                ,data = data_factor_core_clean)

summ(lm_corona)
MODEL INFO:
Observations: 24653 (19 missing obs. deleted)
Dependent Variable: sold_price
Type: OLS linear regression 

MODEL FIT:
F(65,24587) = 740.89, p = 0.00
R² = 0.66
Adj. R² = 0.66 

Standard errors: OLS
---------------------------------------------------------------------------
                                            Est.       S.E.   t val.      p
----------------------------------- ------------ ---------- -------- ------
(Intercept)                            180768.74   57877.60     3.12   0.00
infections_3mma                             9.67       0.55    17.53   0.00
property_typeDUP                       -51453.66   17405.89    -2.96   0.00
property_typeOTH                        24615.51   12134.43     2.03   0.04
property_typePAT                        16034.82    5606.46     2.86   0.00
property_typeSGL                        22833.72    2625.02     8.70   0.00
property_typeTNH                        -3147.82    3330.86    -0.95   0.34
ac_typenone                            -45777.67    2288.13   -20.01   0.00
ac_typenot_central                     -13751.04    1482.97    -9.27   0.00
patio1                                   8117.83     761.13    10.67   0.00
school_general1                         11371.01     979.63    11.61   0.00
photo_count                               914.55      45.30    20.19   0.00
pool1                                   12939.44    1265.41    10.23   0.00
roof_typeother                           3779.46    1402.59     2.69   0.01
roof_typeshingle                        21166.81    1574.80    13.44   0.00
roof_typeslate                          10025.75    6701.58     1.50   0.13
gas_typenatural                        -92572.23   51731.25    -1.79   0.07
gas_typenone                          -132478.82   51704.70    -2.56   0.01
gas_typepropane                       -105428.20   52920.81    -1.99   0.05
gas_typeunknown                       -137409.67   51698.24    -2.66   0.01
out_building1                           -6076.37     823.42    -7.38   0.00
area_living                                32.44       5.46     5.94   0.00
land_acres                               2615.42     924.61     2.83   0.00
appliances1                             24679.44    1030.47    23.95   0.00
garage1                                 11973.79     755.72    15.84   0.00
property_conditionnew                  -24640.48    4675.26    -5.27   0.00
property_conditionother                -20596.44    1010.59   -20.38   0.00
energy_efficient1                       14040.15     847.28    16.57   0.00
exterior_typemetal                        -37.33    2429.54    -0.02   0.99
exterior_typeother                      11897.19    1007.73    11.81   0.00
exterior_typevinyl                       5135.53    1122.19     4.58   0.00
exterior_typewood                        3742.67    1586.37     2.36   0.02
exterior_featurescourtyard              34564.32    8523.08     4.06   0.00
exterior_featuresfence                 -32068.35    3626.83    -8.84   0.00
exterior_featuresnone                  -25089.98    3636.93    -6.90   0.00
exterior_featuresporch                 -32085.30    3718.60    -8.63   0.00
exterior_featurestennis_court            -425.91   10424.65    -0.04   0.97
fireplace1                              11695.55     792.13    14.76   0.00
foundation_typeslab                     14759.89    1150.00    12.83   0.00
foundation_typeunspecified               8375.81    1382.82     6.06   0.00
beds_total1                            -28431.74   19252.36    -1.48   0.14
beds_total2                            -37258.25   19092.77    -1.95   0.05
beds_total3                            -43522.59   19132.21    -2.27   0.02
beds_total4                            -41182.09   19164.64    -2.15   0.03
beds_total5                            -59183.00   19468.00    -3.04   0.00
bath_full1                             -31961.75   20362.52    -1.57   0.12
bath_full2                              -6980.82   20351.01    -0.34   0.73
bath_full3                              19902.15   20411.88     0.98   0.33
bath_full4                              22788.02   22563.01     1.01   0.31
bath_full6                              20194.81   55765.74     0.36   0.72
bath_half1                              14105.18     998.23    14.13   0.00
bath_half2                              38562.72    6450.29     5.98   0.00
bath_half3                              59379.75   36556.86     1.62   0.10
bath_half4                              73612.69   51732.57     1.42   0.15
bath_half5                             -61754.65   29897.51    -2.07   0.04
age                                     -2017.22      65.30   -30.89   0.00
dom                                       -61.39       5.76   -10.65   0.00
sold_date                                   0.50       0.46     1.08   0.28
sewer_typeseptic                        -6656.23    1430.73    -4.65   0.00
sewer_typeunspecified                   -5363.79     778.99    -6.89   0.00
property_stylenot_mobile                68394.40    2090.91    32.71   0.00
subdivision1                             3395.40     912.56     3.72   0.00
water_typewell                           1157.24    3603.27     0.32   0.75
waterfront1                             20298.31    1342.88    15.12   0.00
age_2                                      18.30       0.83    22.16   0.00
area_living_2                               0.01       0.00     6.16   0.00
---------------------------------------------------------------------------
coeftest(lm_corona, vcov = vcovHC(lm_corona, method = "White2", type = "HC0"))

t test of coefficients:

                                 Estimate  Std. Error  t value  Pr(>|t|)    
(Intercept)                    1.8077e+05  3.3119e+04   5.4582 4.857e-08 ***
infections_3mma                9.6711e+00  5.7357e-01  16.8612 < 2.2e-16 ***
property_typeDUP              -5.1454e+04  1.5523e+04  -3.3148 0.0009185 ***
property_typeOTH               2.4616e+04  1.4812e+04   1.6618 0.0965596 .  
property_typePAT               1.6035e+04  5.5605e+03   2.8837 0.0039340 ** 
property_typeSGL               2.2834e+04  2.7157e+03   8.4081 < 2.2e-16 ***
property_typeTNH              -3.1478e+03  3.3429e+03  -0.9416 0.3463877    
ac_typenone                   -4.5778e+04  1.9684e+03 -23.2561 < 2.2e-16 ***
ac_typenot_central            -1.3751e+04  1.5990e+03  -8.5998 < 2.2e-16 ***
patio1                         8.1178e+03  7.7805e+02  10.4335 < 2.2e-16 ***
school_general1                1.1371e+04  1.0385e+03  10.9496 < 2.2e-16 ***
photo_count                    9.1455e+02  4.8880e+01  18.7103 < 2.2e-16 ***
pool1                          1.2939e+04  1.4018e+03   9.2307 < 2.2e-16 ***
roof_typeother                 3.7795e+03  1.4476e+03   2.6109 0.0090365 ** 
roof_typeshingle               2.1167e+04  1.6503e+03  12.8259 < 2.2e-16 ***
roof_typeslate                 1.0026e+04  9.8722e+03   1.0156 0.3098516    
gas_typenatural               -9.2572e+04  3.6053e+03 -25.6768 < 2.2e-16 ***
gas_typenone                  -1.3248e+05  2.4622e+03 -53.8049 < 2.2e-16 ***
gas_typepropane               -1.0543e+05  1.8139e+04  -5.8123 6.237e-09 ***
gas_typeunknown               -1.3741e+05  2.3502e+03 -58.4673 < 2.2e-16 ***
out_building1                 -6.0764e+03  8.2748e+02  -7.3432 2.150e-13 ***
area_living                    3.2442e+01  6.1755e+00   5.2533 1.506e-07 ***
land_acres                     2.6154e+03  9.3633e+02   2.7933 0.0052217 ** 
appliances1                    2.4679e+04  1.1339e+03  21.7658 < 2.2e-16 ***
garage1                        1.1974e+04  7.7362e+02  15.4777 < 2.2e-16 ***
property_conditionnew         -2.4640e+04  6.4509e+03  -3.8197 0.0001340 ***
property_conditionother       -2.0596e+04  9.4498e+02 -21.7957 < 2.2e-16 ***
energy_efficient1              1.4040e+04  8.4263e+02  16.6623 < 2.2e-16 ***
exterior_typemetal            -3.7329e+01  2.3648e+03  -0.0158 0.9874058    
exterior_typeother             1.1897e+04  1.0773e+03  11.0436 < 2.2e-16 ***
exterior_typevinyl             5.1355e+03  1.1145e+03   4.6080 4.086e-06 ***
exterior_typewood              3.7427e+03  1.7848e+03   2.0970 0.0360022 *  
exterior_featurescourtyard     3.4564e+04  1.4123e+04   2.4474 0.0143969 *  
exterior_featuresfence        -3.2068e+04  5.3581e+03  -5.9850 2.194e-09 ***
exterior_featuresnone         -2.5090e+04  5.3651e+03  -4.6766 2.933e-06 ***
exterior_featuresporch        -3.2085e+04  5.4215e+03  -5.9182 3.299e-09 ***
exterior_featurestennis_court -4.2591e+02  1.0542e+04  -0.0404 0.9677739    
fireplace1                     1.1696e+04  8.3445e+02  14.0158 < 2.2e-16 ***
foundation_typeslab            1.4760e+04  1.2931e+03  11.4146 < 2.2e-16 ***
foundation_typeunspecified     8.3758e+03  1.4303e+03   5.8559 4.806e-09 ***
beds_total1                   -2.8432e+04  2.5251e+04  -1.1260 0.2601957    
beds_total2                   -3.7258e+04  2.5163e+04  -1.4807 0.1387039    
beds_total3                   -4.3523e+04  2.5227e+04  -1.7253 0.0844946 .  
beds_total4                   -4.1182e+04  2.5265e+04  -1.6300 0.1031127    
beds_total5                   -5.9183e+04  2.5704e+04  -2.3025 0.0213170 *  
bath_full1                    -3.1962e+04  2.4096e+04  -1.3264 0.1847120    
bath_full2                    -6.9808e+03  2.4086e+04  -0.2898 0.7719459    
bath_full3                     1.9902e+04  2.4179e+04   0.8231 0.4104509    
bath_full4                     2.2788e+04  3.0301e+04   0.7521 0.4520199    
bath_full6                     2.0195e+04  2.4906e+04   0.8108 0.4174683    
bath_half1                     1.4105e+04  1.1369e+03  12.4062 < 2.2e-16 ***
bath_half2                     3.8563e+04  7.8980e+03   4.8826 1.054e-06 ***
bath_half3                     5.9380e+04  1.0913e+04   5.4414 5.336e-08 ***
bath_half4                     7.3613e+04  3.2038e+03  22.9767 < 2.2e-16 ***
bath_half5                    -6.1755e+04  2.7625e+04  -2.2355 0.0253948 *  
age                           -2.0172e+03  8.4747e+01 -23.8030 < 2.2e-16 ***
dom                           -6.1395e+01  5.7883e+00 -10.6067 < 2.2e-16 ***
sold_date                      4.9735e-01  4.7529e-01   1.0464 0.2953845    
sewer_typeseptic              -6.6562e+03  1.4638e+03  -4.5472 5.463e-06 ***
sewer_typeunspecified         -5.3638e+03  7.5612e+02  -7.0938 1.340e-12 ***
property_stylenot_mobile       6.8394e+04  1.7615e+03  38.8270 < 2.2e-16 ***
subdivision1                   3.3954e+03  9.2014e+02   3.6901 0.0002247 ***
water_typewell                 1.1572e+03  4.0744e+03   0.2840 0.7763914    
waterfront1                    2.0298e+04  1.5074e+03  13.4654 < 2.2e-16 ***
age_2                          1.8303e+01  1.1918e+00  15.3579 < 2.2e-16 ***
area_living_2                  8.9354e-03  1.7703e-03   5.0473 4.512e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# Visualizing marginal effect per positive tests on price
lm_corona_single <- lm(sold_price ~ infections_3mma 
                
                ,data = data_factor_core_clean)
summ(lm_corona_single)    
MODEL INFO:
Observations: 24672
Dependent Variable: sold_price
Type: OLS linear regression 

MODEL FIT:
F(1,24670) = 1003.58, p = 0.00
R² = 0.04
Adj. R² = 0.04 

Standard errors: OLS
----------------------------------------------------------
                             Est.     S.E.   t val.      p
--------------------- ----------- -------- -------- ------
(Intercept)             162738.68   618.07   263.30   0.00
infections_3mma             21.53     0.68    31.68   0.00
----------------------------------------------------------
ggpredict_1 <- ggpredict(lm_corona, terms = "infections_3mma")
ggpredict_2 <- ggpredict(lm_corona_single, terms = "infections_3mma")

# Plots
ggplot(data_factor_core, aes(x = infections_3mma)) +
   geom_smooth(data_factor_core, mapping = aes(y = sold_price), color = "grey50") + # Actual Data
   geom_smooth(ggpredict_1, mapping = aes(x, predicted), linetype = "dashed", color = "darkred") + # Controlled model
   geom_smooth(ggpredict_2, mapping = aes(x, predicted), linetype = "dashed", color = "darkblue") + # Best single fit
   ggtitle("Model Fit Overview") 
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

 
# Predicting infections with house prices
lm_flip <- lm_flip <- lm(infections_3mma ~ sold_price , data = data_factor)
summ(lm_flip)
MODEL INFO:
Observations: 24672
Dependent Variable: infections_3mma
Type: OLS linear regression 

MODEL FIT:
F(1,24670) = 1003.58, p = 0.00
R² = 0.04
Adj. R² = 0.04 

Standard errors: OLS
-------------------------------------------------
                     Est.    S.E.   t val.      p
----------------- ------- ------- -------- ------
(Intercept)         92.28   11.06     8.34   0.00
sold_price           0.00    0.00    31.68   0.00
-------------------------------------------------
ggpredict_flip <- ggpredict(lm_flip, terms = "sold_price")

ggplot(data_factor, aes(x = sold_price)) +
   geom_smooth(data_factor, mapping = aes(y = infections_3mma), color = "grey50") +
   geom_smooth(ggpredict_flip, mapping = aes(x, predicted), linetype = "dashed", color = "darkred") +
   labs(title = "Flipped Regression", subtitle = "Explining Infections using Variations in Price",
         caption = "") 
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
`geom_smooth()` using method = 'loess' and formula 'y ~ x'


2.2 Corona on Number of Bedrooms
2.2.1 Visualiztion

# Distribution
# Find the mean of each group
library(plyr)
data_factor$beds_total <- as.numeric(data_factor$beds_total)
room_mean <- ddply(data_factor, "infections_period", summarise, beds_mean=mean(beds_total, na.rm = TRUE))

data_factor$beds_total <- as.numeric(data_factor$beds_total)
a <- ggplot(data_factor, aes(x=beds_total, fill = infections_period)) +
    geom_density(alpha = 0.5, position = "identity") +
    scale_fill_manual(values=c(very_low, med)) +
    labs(title = "Distibution of Number of Bedrooms") +
    geom_vline(data=room_mean, aes(xintercept = room_mean[2,2]), linetype="dashed", size= 0.4, color = very_low, alpha = 0.5) +
    geom_vline(data=room_mean, aes(xintercept = room_mean[1,2]), linetype="dashed", size= 0.4, alpha = 0.5) +
    xlab("Number of Bedrooms") +
    ylab("Density") +
    labs(fill = "Infection Period")


# Distribution of total price and number of beds
data_factor$beds_total <- as.factor(data_factor$beds_total)
b <- ggplot(data = subset(data_factor, !is.na(beds_total)), aes(x = beds_total, y = sold_price, fill = beds_total)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    #coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)) +
      labs(title = "Distributions of Sold Price by Number of Bedrooms",
         caption = "") +
      xlab("Number of Bedrooms") +
      ylab("Sold Price")

      #+
      #scale_fill_manual(values = c(very_low, med), 
      #                name = "Infection Period",
      #                labels = c("Pre", "Post"))

# Distribution of price and number of beds before and after corona period
c <- ggplot(data = subset(data_factor, !is.na(beds_total)), aes(x = beds_total, y = sold_price, fill = beds_total)) +
    geom_violin(data = subset(data_factor, !is.na(beds_total)), mapping = aes(alpha = 0.5, fill = infections_period)) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    #coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)) +
      labs(title = "Distributions of Sold Price by Number of Bedrooms", 
           subtitle = "Price Pre vs. Post Infection Period",
           caption = "") +
      xlab("Number of Bedrooms")  +
      ylab("Sold Price")

# Distribution of price per sqft. and number of beds
data_factor$beds_total <- as.factor(data_factor$beds_total)
d <- ggplot(data = subset(data_factor, !is.na(beds_total)), aes(x = beds_total, y = sold_price/area_living, fill = beds_total)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    #coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)) +
      labs( title = "Distributions of Sold Price by Number of Bedrooms", subtitle = "Sold Price Per Sqft.",
         caption = "") +
      xlab("Number of Bedrooms") +
      ylab("Sold Price per Sqft.")
  

# Distribution of price per sqft. and number of beds before and after corona period
e <- ggplot(data = subset(data_factor, !is.na(beds_total)), aes(x = beds_total, y = sold_price/area_living , fill = beds_total)) +
    geom_violin(data = subset(data_factor, !is.na(beds_total)), mapping = aes(alpha = 0.5, fill = infections_period)) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    #coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)) +
      labs( title = "Distributions of Sold Price by Number of Bedrooms", subtitle = "Sold Price Per Sqft. Pre vs. Post Infection Period",
         caption = "") +
      xlab("Number of Bedrooms")  +
      ylab("Sold Price per Sqft.")

gridExtra::grid.arrange(a)

gridExtra::grid.arrange(b)

gridExtra::grid.arrange(c)

gridExtra::grid.arrange(d)

gridExtra::grid.arrange(e)

#gridExtra::grid.arrange(b,c, ncol = 2)
2.2.2 Modeling

Ideas

  • Break into each room number
coeftest(lm_corona_bedrooms, vcov = vcovHC(lm_corona_bedrooms, method = "White2", type = "HC0"))

t test of coefficients:

                                           Estimate  Std. Error  t value  Pr(>|t|)    
(Intercept)                              2.4776e+05  2.9950e+04   8.2726 < 2.2e-16 ***
ac_typenone                             -5.6116e+04  1.9719e+03 -28.4581 < 2.2e-16 ***
ac_typenot_central                      -2.1396e+04  1.7984e+03 -11.8972 < 2.2e-16 ***
patio1                                   1.2550e+04  8.6935e+02  14.4358 < 2.2e-16 ***
school_general1                          9.5793e+03  1.1390e+03   8.4102 < 2.2e-16 ***
photo_count                              1.3164e+03  5.2185e+01  25.2250 < 2.2e-16 ***
pool1                                    2.1208e+04  1.5874e+03  13.3605 < 2.2e-16 ***
roof_typeother                           7.7967e+03  1.5121e+03   5.1561 2.541e-07 ***
roof_typeshingle                         2.8462e+04  1.7549e+03  16.2185 < 2.2e-16 ***
roof_typeslate                           1.8896e+04  9.9617e+03   1.8969 0.0578545 .  
gas_typenatural                         -1.0107e+05  3.6709e+03 -27.5316 < 2.2e-16 ***
gas_typenone                            -1.4202e+05  2.3211e+03 -61.1860 < 2.2e-16 ***
gas_typepropane                         -9.9809e+04  1.8136e+04  -5.5034 3.763e-08 ***
gas_typeunknown                         -1.4162e+05  2.1590e+03 -65.5982 < 2.2e-16 ***
out_building1                           -5.8258e+03  9.2193e+02  -6.3192 2.675e-10 ***
appliances1                              2.5486e+04  1.2330e+03  20.6704 < 2.2e-16 ***
property_conditionnew                   -2.3228e+04  6.7244e+03  -3.4543 0.0005527 ***
property_conditionother                 -2.1236e+04  1.0801e+03 -19.6609 < 2.2e-16 ***
energy_efficient1                        1.9237e+04  9.2813e+02  20.7269 < 2.2e-16 ***
exterior_typemetal                      -3.9464e+03  2.5047e+03  -1.5756 0.1151323    
exterior_typeother                       1.4494e+04  1.2073e+03  12.0049 < 2.2e-16 ***
exterior_typevinyl                       3.2328e+03  1.2554e+03   2.5751 0.0100260 *  
exterior_typewood                        2.1060e+03  1.9850e+03   1.0609 0.2887255    
exterior_featurescourtyard               3.7099e+04  1.4742e+04   2.5166 0.0118550 *  
exterior_featuresfence                  -3.2078e+04  5.9856e+03  -5.3593 8.430e-08 ***
exterior_featuresnone                   -2.2204e+04  5.9969e+03  -3.7026 0.0002139 ***
exterior_featuresporch                  -2.8730e+04  6.0661e+03  -4.7361 2.191e-06 ***
exterior_featurestennis_court            1.2094e+04  1.3905e+04   0.8698 0.3844197    
fireplace1                               3.2917e+04  8.6870e+02  37.8919 < 2.2e-16 ***
foundation_typeslab                      2.0097e+04  1.3612e+03  14.7636 < 2.2e-16 ***
foundation_typeunspecified               9.3882e+03  1.5132e+03   6.2042 5.587e-10 ***
beds_total1                             -6.9914e+04  2.9402e+04  -2.3779 0.0174185 *  
beds_total2                             -5.0169e+04  2.9144e+04  -1.7214 0.0851827 .  
beds_total3                             -2.4161e+04  2.9143e+04  -0.8291 0.4070831    
beds_total4                              1.7109e+04  2.9166e+04   0.5866 0.5574625    
beds_total5                              2.6766e+04  2.9743e+04   0.8999 0.3681774    
age                                     -2.2840e+03  8.5578e+01 -26.6889 < 2.2e-16 ***
dom                                     -3.5794e+01  6.3773e+00  -5.6128 2.012e-08 ***
sewer_typeseptic                        -4.8724e+03  1.5741e+03  -3.0955 0.0019673 ** 
sewer_typeunspecified                   -5.1675e+03  8.4585e+02  -6.1093 1.016e-09 ***
property_stylenot_mobile                 7.3419e+04  1.8556e+03  39.5664 < 2.2e-16 ***
subdivision1                             2.4097e+03  1.0162e+03   2.3712 0.0177365 *  
water_typewell                          -1.7269e+03  4.7569e+03  -0.3630 0.7165787    
waterfront1                              2.9108e+04  1.7101e+03  17.0217 < 2.2e-16 ***
age_2                                    2.0720e+01  1.1683e+00  17.7342 < 2.2e-16 ***
data_factor$infections_3mma             -2.7000e+01  1.5318e+01  -1.7626 0.0779746 .  
beds_total1:data_factor$infections_3mma  2.3287e+01  1.5739e+01   1.4796 0.1390006    
beds_total2:data_factor$infections_3mma  3.1473e+01  1.5368e+01   2.0480 0.0405680 *  
beds_total3:data_factor$infections_3mma  3.5902e+01  1.5330e+01   2.3420 0.0191906 *  
beds_total4:data_factor$infections_3mma  3.6758e+01  1.5373e+01   2.3911 0.0168045 *  
beds_total5:data_factor$infections_3mma  4.5991e+01  1.6466e+01   2.7931 0.0052244 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


2.3 Corona on Price Quantiles
2.3.1 Visualization

# Find the mean of each group
library(plyr)
price_means <- ddply(data_factor, "infections_period", summarise, price_mean = mean(sold_price, na.rm = TRUE))

# Distribution: Total
ggplot(data_factor, aes(x = sold_price)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    ggtitle("Price Distribution") +
    geom_vline(data=price_means, aes(xintercept = mean(sold_price)), linetype="dashed", size= 0.4, color = very_low, alpha = 0.8) +
    xlab("Sold Price") +
    ylab("Density") 


# Distribution: Infection
ggplot(data_factor, aes(x = sold_price, fill = infections_period)) +
    geom_density(alpha = 0.5, position = "identity") +
    ggtitle("Price Distributions") +
    geom_vline(data=price_means, aes(xintercept = price_means[2,2]), linetype="dashed", size= 0.4, color = med, alpha = 0.8) +
    geom_vline(data = price_means, aes(xintercept = price_means[1,2]), linetype="dashed", size= 0.4, color = very_low, alpha = 0.8) +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post")) +
    xlab("Sold Price") +
    ylab("Density") +
    labs(fill = "Infection Period")


# Distribution: Top vs. Bottom
ggplot(data_factor) +
    geom_density(aes(x = sold_price, fill = infections_period), alpha = 0.5, position = "identity") + 
    facet_grid(vars(top25_sold_price, bottom25_sold_price), scales = "free") +
    ggtitle("Price Distributions") +
    scale_fill_manual(values=c(very_low, med)) +
    xlab("Sold Price") +
    labs(fill = "Infection Period") +
    ylab("Density") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

#Price and Infections
ggplot(data_factor, aes(x = infections_period, y = sold_price, fill = infections_period)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)) +
    ggtitle("Comparison of Sold Price") +
    xlab("Infection Period") +
    scale_fill_manual(values=c(very_low, med)) +
    ylab("Sold Price") 
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.


2.3.2 Modeling
coeftest(lm_corona_price_bottom, vcov = vcovHC(lm_corona_price_bottom, method = "White2", type = "HC0"))

t test of coefficients:

                                                   Estimate  Std. Error  t value  Pr(>|t|)    
(Intercept)                                      2.7857e+05  2.2480e+04  12.3920 < 2.2e-16 ***
property_typeDUP                                -2.1975e+04  1.6374e+04  -1.3421 0.1795756    
property_typeOTH                                 1.4584e+04  1.2380e+04   1.1781 0.2387656    
property_typePAT                                 1.0163e+04  4.9850e+03   2.0388 0.0414796 *  
property_typeSGL                                 1.8552e+04  2.3641e+03   7.8474 4.420e-15 ***
property_typeTNH                                -4.4440e+03  3.0234e+03  -1.4699 0.1416099    
ac_typenone                                     -2.5552e+04  1.3619e+03 -18.7624 < 2.2e-16 ***
ac_typenot_central                              -3.3607e+03  1.2777e+03  -2.6302 0.0085391 ** 
patio1                                           4.1710e+03  6.7317e+02   6.1960 5.882e-10 ***
school_general1                                  7.7967e+03  8.8654e+02   8.7945 < 2.2e-16 ***
photo_count                                      5.6804e+02  4.0920e+01  13.8818 < 2.2e-16 ***
pool1                                            1.1680e+04  1.3071e+03   8.9360 < 2.2e-16 ***
roof_typeother                                  -2.7074e+02  1.1928e+03  -0.2270 0.8204422    
roof_typeshingle                                 1.1414e+04  1.4048e+03   8.1253 4.672e-16 ***
roof_typeslate                                   5.8497e+03  8.3957e+03   0.6967 0.4859696    
gas_typenatural                                 -6.6364e+04  3.2084e+03 -20.6848 < 2.2e-16 ***
gas_typenone                                    -1.0699e+05  2.1142e+03 -50.6067 < 2.2e-16 ***
gas_typepropane                                 -7.3202e+04  1.5062e+04  -4.8601 1.181e-06 ***
gas_typeunknown                                 -1.0843e+05  2.0384e+03 -53.1917 < 2.2e-16 ***
out_building1                                   -6.6969e+03  7.1902e+02  -9.3140 < 2.2e-16 ***
area_living                                     -1.8309e+01  5.5182e+00  -3.3179 0.0009081 ***
land_acres                                       1.6068e+03  7.6658e+02   2.0960 0.0360904 *  
appliances1                                      1.0363e+04  8.8813e+02  11.6681 < 2.2e-16 ***
garage1                                          6.9436e+03  6.6603e+02  10.4254 < 2.2e-16 ***
property_conditionnew                           -9.6468e+03  5.8477e+03  -1.6497 0.0990228 .  
property_conditionother                         -1.0477e+04  8.6301e+02 -12.1400 < 2.2e-16 ***
energy_efficient1                                1.0733e+04  7.4567e+02  14.3938 < 2.2e-16 ***
exterior_typemetal                              -6.8533e+02  1.9199e+03  -0.3570 0.7211248    
exterior_typeother                               8.7693e+03  9.3197e+02   9.4094 < 2.2e-16 ***
exterior_typevinyl                               2.1807e+03  9.6058e+02   2.2702 0.0232035 *  
exterior_typewood                                3.6714e+03  1.4550e+03   2.5232 0.0116338 *  
exterior_featurescourtyard                       2.3334e+04  1.2856e+04   1.8151 0.0695255 .  
exterior_featuresfence                          -3.1847e+04  4.8489e+03  -6.5679 5.206e-11 ***
exterior_featuresnone                           -2.7200e+04  4.8484e+03  -5.6101 2.043e-08 ***
exterior_featuresporch                          -3.1926e+04  4.8992e+03  -6.5165 7.334e-11 ***
exterior_featurestennis_court                   -8.2497e+03  9.9486e+03  -0.8292 0.4069776    
fireplace1                                       1.0616e+04  7.1079e+02  14.9359 < 2.2e-16 ***
foundation_typeslab                              4.0988e+03  1.0575e+03   3.8759 0.0001065 ***
foundation_typeunspecified                       2.1203e+03  1.1394e+03   1.8609 0.0627696 .  
beds_total1                                     -6.9770e+03  2.1265e+04  -0.3281 0.7428376    
beds_total2                                     -1.3761e+04  2.1173e+04  -0.6499 0.5157434    
beds_total3                                     -2.2255e+04  2.1202e+04  -1.0497 0.2938691    
beds_total4                                     -1.7414e+04  2.1228e+04  -0.8203 0.4120491    
beds_total5                                     -3.5118e+04  2.1647e+04  -1.6223 0.1047492    
bath_full1                                      -1.6852e+04  1.3865e+04  -1.2154 0.2242251    
bath_full2                                      -8.9356e+03  1.3844e+04  -0.6454 0.5186491    
bath_full3                                       1.6747e+04  1.3974e+04   1.1984 0.2307573    
bath_full4                                       1.5693e+04  2.1486e+04   0.7304 0.4651680    
bath_full6                                       4.0308e+04  1.4910e+04   2.7034 0.0068690 ** 
bath_half1                                       1.3388e+04  1.0236e+03  13.0790 < 2.2e-16 ***
bath_half2                                       3.0999e+04  8.0343e+03   3.8584 0.0001144 ***
bath_half3                                       5.9627e+04  8.9836e+03   6.6373 3.262e-11 ***
bath_half4                                       9.4038e+04  2.9045e+03  32.3760 < 2.2e-16 ***
bath_half5                                      -3.1271e+04  2.1658e+04  -1.4439 0.1487886    
age                                             -1.6200e+03  7.0930e+01 -22.8398 < 2.2e-16 ***
dom                                             -4.0071e+01  4.9024e+00  -8.1738 3.132e-16 ***
sewer_typeseptic                                -6.3326e+03  1.2071e+03  -5.2461 1.567e-07 ***
sewer_typeunspecified                           -4.9833e+03  6.5197e+02  -7.6433 2.194e-14 ***
property_stylenot_mobile                         2.8897e+04  1.5948e+03  18.1201 < 2.2e-16 ***
subdivision1                                     2.2100e+03  7.7041e+02   2.8686 0.0041258 ** 
water_typewell                                   2.8814e+03  3.4386e+03   0.8379 0.4020708    
waterfront1                                      1.7854e+04  1.3436e+03  13.2880 < 2.2e-16 ***
age_2                                            1.4555e+01  9.7616e-01  14.9108 < 2.2e-16 ***
area_living_2                                    1.9719e-02  1.6137e-03  12.2194 < 2.2e-16 ***
data_factor$infections_3mma                      8.6517e+00  4.9113e-01  17.6161 < 2.2e-16 ***
bottom25_sold_price                             -7.9843e+04  8.2900e+02 -96.3119 < 2.2e-16 ***
data_factor$infections_3mma:bottom25_sold_price -6.2653e+00  7.9397e-01  -7.8911 3.120e-15 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


2.4 Corona on Age Quantiles
2.4.1 Visualization
# Conditional Mean
library(plyr)
age_mean_data <- ddply(data_factor, "infections_period", summarise, age_mean = mean(age, na.rm = TRUE))

# Distribution: Total
ggplot(data_factor, aes(x = age)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    ggtitle("Age Distribution") +
    geom_vline(aes(xintercept = mean(age)), linetype="dashed", size= 0.4, alpha = 0.5, color = very_low) +
    xlab("Age of Property") +
    ylab("Density")



# Distribution: Infection
ggplot(data_factor, aes(x = age, fill = infections_period)) +
    geom_density(alpha = 0.5, position = "identity") +
    ggtitle("Age Distributions") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post")) +
    geom_vline(data = age_mean_data, aes(xintercept = age_mean_data[2,2]), linetype="dashed", size= 0.5, color = med, alpha = 0.8) +
    geom_vline(data = age_mean_data, aes(xintercept = age_mean_data[1,2]), linetype="dashed", size= 0.5, alpha = 0.8, color = very_low) +
    xlab("Age of Property") +
    ylab("Density")


?scale_fill_discrete()

# Distribution: Top vs. Bottom
ggplot(data_factor) +
    geom_density(aes(x = age, fill = infections_period), alpha = 0.5, position = "identity") + 
                     facet_grid(vars(top25_age, bottom25_age), scales = "free") +
                     ggtitle("Age Distributions") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post")) +
    labs(fill = "Infection Period") +
    xlab("Age of Property") +
    ylab("Density")


#Age on Infections
ggplot(data_factor, aes(x = infections_period, y = age, fill = infections_period)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)) +
    ggtitle("Comparison of Age") +
    xlab("Infection Period") +
    ylab("Age of Property") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

2.4.2 Modeling
coeftest(lm_corona_age_bottom, vcov = vcovHC(lm_corona_age_bottom, method = "White2", type = "HC0"))

t test of coefficients:

                                            Estimate  Std. Error  t value  Pr(>|t|)    
(Intercept)                               1.3341e+05  3.2081e+04   4.1586 3.213e-05 ***
ac_typenone                              -4.5686e+04  1.9758e+03 -23.1223 < 2.2e-16 ***
ac_typenot_central                       -1.3725e+04  1.6034e+03  -8.5599 < 2.2e-16 ***
patio1                                    8.8777e+03  7.8834e+02  11.2611 < 2.2e-16 ***
school_general1                           1.2233e+04  1.0519e+03  11.6291 < 2.2e-16 ***
photo_count                               8.3004e+02  4.9235e+01  16.8587 < 2.2e-16 ***
pool1                                     9.7246e+03  1.4036e+03   6.9282 4.368e-12 ***
roof_typeother                            3.4687e+03  1.4601e+03   2.3756 0.0175271 *  
roof_typeshingle                          2.2548e+04  1.6702e+03  13.5004 < 2.2e-16 ***
roof_typeslate                            1.0875e+04  9.8409e+03   1.1051 0.2691232    
gas_typenatural                          -8.5473e+04  3.7766e+03 -22.6323 < 2.2e-16 ***
gas_typenone                             -1.2673e+05  2.5713e+03 -49.2845 < 2.2e-16 ***
gas_typepropane                          -9.7511e+04  1.8583e+04  -5.2472 1.557e-07 ***
gas_typeunknown                          -1.3010e+05  2.4694e+03 -52.6863 < 2.2e-16 ***
out_building1                            -6.6527e+03  8.3095e+02  -8.0061 1.236e-15 ***
land_acres                                3.1612e+03  9.5672e+02   3.3042 0.0009539 ***
appliances1                               2.5020e+04  1.1462e+03  21.8287 < 2.2e-16 ***
garage1                                   1.4086e+04  7.7556e+02  18.1622 < 2.2e-16 ***
property_conditionnew                    -6.1690e+03  6.6511e+03  -0.9275 0.3536702    
property_conditionother                  -2.0673e+04  9.6156e+02 -21.4998 < 2.2e-16 ***
energy_efficient1                         1.5373e+04  8.5713e+02  17.9354 < 2.2e-16 ***
exterior_typemetal                       -2.4707e+02  2.4198e+03  -0.1021 0.9186751    
exterior_typeother                        1.2851e+04  1.1016e+03  11.6659 < 2.2e-16 ***
exterior_typevinyl                        5.8691e+03  1.1316e+03   5.1867 2.157e-07 ***
exterior_typewood                         4.8121e+03  1.8249e+03   2.6370 0.0083702 ** 
exterior_featurescourtyard                4.5337e+04  1.5197e+04   2.9833 0.0028542 ** 
exterior_featuresfence                   -2.2494e+04  5.5120e+03  -4.0810 4.499e-05 ***
exterior_featuresnone                    -1.4390e+04  5.5240e+03  -2.6049 0.0091955 ** 
exterior_featuresporch                   -2.0275e+04  5.5771e+03  -3.6353 0.0002782 ***
exterior_featurestennis_court             8.8427e+03  1.0725e+04   0.8245 0.4096642    
fireplace1                                1.1842e+04  8.3865e+02  14.1201 < 2.2e-16 ***
foundation_typeslab                       1.2592e+04  1.3040e+03   9.6567 < 2.2e-16 ***
foundation_typeunspecified                6.6525e+03  1.4485e+03   4.5926 4.400e-06 ***
beds_total1                              -2.4476e+04  2.7523e+04  -0.8893 0.3738489    
beds_total2                              -2.5550e+04  2.7331e+04  -0.9348 0.3498804    
beds_total3                              -2.4087e+04  2.7328e+04  -0.8814 0.3781066    
beds_total4                              -2.0511e+04  2.7360e+04  -0.7497 0.4534551    
beds_total5                              -3.9284e+04  2.7797e+04  -1.4133 0.1575900    
bath_full1                               -3.8188e+04  2.4744e+04  -1.5433 0.1227657    
bath_full2                               -1.2393e+04  2.4737e+04  -0.5010 0.6163953    
bath_full3                                1.2200e+04  2.4816e+04   0.4916 0.6229929    
bath_full4                                1.3770e+04  3.0986e+04   0.4444 0.6567506    
bath_full6                               -7.2112e+03  2.5341e+04  -0.2846 0.7759828    
bath_half1                                1.2440e+04  1.1424e+03  10.8891 < 2.2e-16 ***
bath_half2                                3.7417e+04  7.6533e+03   4.8890 1.020e-06 ***
bath_half3                                6.4543e+04  8.3666e+03   7.7144 1.261e-14 ***
bath_half4                                7.6590e+04  3.2113e+03  23.8501 < 2.2e-16 ***
bath_half5                               -5.6216e+04  2.5008e+04  -2.2479 0.0245917 *  
dom                                      -6.2854e+01  5.8220e+00 -10.7959 < 2.2e-16 ***
sold_date                                 1.4337e+00  4.6958e-01   3.0531 0.0022670 ** 
sewer_typeseptic                         -6.4102e+03  1.4716e+03  -4.3560 1.330e-05 ***
sewer_typeunspecified                    -4.3032e+03  7.5897e+02  -5.6697 1.446e-08 ***
property_stylenot_mobile                  6.9807e+04  1.7731e+03  39.3696 < 2.2e-16 ***
subdivision1                              3.1875e+03  9.3573e+02   3.4065 0.0006592 ***
water_typewell                            2.0497e+02  4.1549e+03   0.0493 0.9606545    
waterfront1                               2.0545e+04  1.5256e+03  13.4665 < 2.2e-16 ***
area_living_2                             1.7648e-02  4.2033e-04  41.9858 < 2.2e-16 ***
data_factor$infections_3mma               9.0607e+00  7.2102e-01  12.5664 < 2.2e-16 ***
bottom25_age                              2.5730e+04  9.6686e+02  26.6121 < 2.2e-16 ***
data_factor$infections_3mma:bottom25_age  1.5468e+00  9.1535e-01   1.6898 0.0910714 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


2.5 Corona on Size Quantiles
2.5.1 Visualization
# Conditional Mean
library(plyr)
area_living_mean_data <- ddply(data_factor, "infections_period", summarise, area_living_mean = mean(area_living, na.rm = TRUE))

# Distribution: Total
ggplot(data_factor, aes(x = area_living)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    ggtitle("Living Area Distribution") +
    geom_vline(aes(xintercept = mean(area_living)), linetype="dashed", size= 0.4, alpha = 0.5, color = very_low) +
    xlab("Living Area") +
    ylab("Density")



# Distribution: Infection
ggplot(data_factor, aes(x = area_living, fill = infections_period)) +
    geom_density(alpha = 0.5, position = "identity") +
    ggtitle("Living Area Distributions") +
    geom_vline(data = area_living_mean_data, aes(xintercept = area_living_mean_data[2,2]), linetype="dashed", size= 0.5, color = med, alpha = 0.8) +
    geom_vline(data = area_living_mean_data, aes(xintercept = area_living_mean_data[1,2]), linetype="dashed", size= 0.5, alpha = 0.8, color = very_low) +
    xlab("Living Area") +
    ylab("Density") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))


# Distribution: Top vs. Bottom
ggplot(data_factor) +
    geom_density(aes(x = area_living, fill = infections_period), alpha = 0.5, position = "identity") + 
    facet_grid(vars(top25_area_living, bottom25_area_living), scales = "free") +
    ggtitle("Living Area Distributions") +
    xlab("Living Area") +
    ylab("Density") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))


#area_living on Infections
ggplot(data_factor, aes(x = infections_period, y = area_living, fill = infections_period)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)) +
    ggtitle("Comparison of Living Area") +
    xlab("Infection Period") +
    ylab("Living Area") +
    scale_fill_manual(values=c(very_low, med))
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

2.5.2 Modeling
coeftest(lm_corona_area_living_bottom, vcov = vcovHC(lm_corona_area_living_bottom, method = "White2", type = "HC0"))

t test of coefficients:

                                                    Estimate  Std. Error  t value  Pr(>|t|)    
(Intercept)                                       2.1339e+05  3.3909e+04   6.2930 3.167e-10 ***
property_typeDUP                                 -4.1450e+04  1.3956e+04  -2.9700  0.002980 ** 
property_typeOTH                                  3.2243e+04  1.5969e+04   2.0191  0.043490 *  
property_typePAT                                  1.6802e+04  6.1298e+03   2.7411  0.006129 ** 
property_typeSGL                                  3.1499e+04  2.8575e+03  11.0232 < 2.2e-16 ***
property_typeTNH                                 -1.0751e+03  3.6228e+03  -0.2968  0.766651    
ac_typenone                                      -4.5749e+04  1.9656e+03 -23.2750 < 2.2e-16 ***
ac_typenot_central                               -1.3048e+04  1.6571e+03  -7.8737 3.585e-15 ***
patio1                                            8.2691e+03  8.1088e+02  10.1977 < 2.2e-16 ***
school_general1                                   1.0025e+04  1.0796e+03   9.2861 < 2.2e-16 ***
photo_count                                       1.0654e+03  5.1381e+01  20.7345 < 2.2e-16 ***
pool1                                             1.7630e+04  1.4758e+03  11.9461 < 2.2e-16 ***
roof_typeother                                    5.8738e+03  1.4607e+03   4.0213 5.805e-05 ***
roof_typeshingle                                  2.4781e+04  1.6803e+03  14.7482 < 2.2e-16 ***
roof_typeslate                                    1.5016e+04  1.0401e+04   1.4437  0.148832    
gas_typenatural                                  -7.2088e+04  3.6703e+03 -19.6407 < 2.2e-16 ***
gas_typenone                                     -1.1453e+05  2.5305e+03 -45.2579 < 2.2e-16 ***
gas_typepropane                                  -8.4275e+04  1.7473e+04  -4.8232 1.421e-06 ***
gas_typeunknown                                  -1.1604e+05  2.4100e+03 -48.1506 < 2.2e-16 ***
out_building1                                    -5.3839e+03  8.6629e+02  -6.2148 5.220e-10 ***
land_acres                                        5.1387e+03  9.7520e+02   5.2694 1.380e-07 ***
appliances1                                       2.3783e+04  1.1649e+03  20.4166 < 2.2e-16 ***
garage1                                           1.3445e+04  8.0352e+02  16.7324 < 2.2e-16 ***
property_conditionnew                            -2.4761e+04  6.4570e+03  -3.8347  0.000126 ***
property_conditionother                          -2.0736e+04  9.9837e+02 -20.7701 < 2.2e-16 ***
energy_efficient1                                 1.4537e+04  8.7617e+02  16.5912 < 2.2e-16 ***
exterior_typemetal                               -1.8255e+03  2.3499e+03  -0.7768  0.437268    
exterior_typeother                                1.2592e+04  1.1204e+03  11.2388 < 2.2e-16 ***
exterior_typevinyl                                3.1157e+03  1.1613e+03   2.6830  0.007302 ** 
exterior_typewood                                 3.0180e+03  1.8587e+03   1.6237  0.104449    
exterior_featurescourtyard                        3.7328e+04  1.3936e+04   2.6785  0.007400 ** 
exterior_featuresfence                           -3.2656e+04  5.6094e+03  -5.8217 5.897e-09 ***
exterior_featuresnone                            -2.5255e+04  5.6117e+03  -4.5004 6.814e-06 ***
exterior_featuresporch                           -3.2058e+04  5.6736e+03  -5.6505 1.618e-08 ***
exterior_featurestennis_court                     5.4193e+03  1.2188e+04   0.4447  0.656574    
fireplace1                                        2.0496e+04  8.3541e+02  24.5339 < 2.2e-16 ***
foundation_typeslab                               1.3667e+04  1.3123e+03  10.4143 < 2.2e-16 ***
foundation_typeunspecified                        7.7983e+03  1.4477e+03   5.3867 7.242e-08 ***
beds_total1                                      -2.4930e+04  2.6346e+04  -0.9463  0.344019    
beds_total2                                      -2.8425e+04  2.6173e+04  -1.0860  0.277468    
beds_total3                                      -3.1603e+04  2.6194e+04  -1.2065  0.227639    
beds_total4                                      -1.3294e+04  2.6225e+04  -0.5069  0.612212    
beds_total5                                      -1.7741e+04  2.6701e+04  -0.6644  0.506431    
bath_full1                                       -4.9882e+04  2.7936e+04  -1.7856  0.074177 .  
bath_full2                                       -1.2439e+04  2.7934e+04  -0.4453  0.656094    
bath_full3                                        4.0427e+04  2.8004e+04   1.4436  0.148852    
bath_full4                                        5.4599e+04  3.4427e+04   1.5859  0.112770    
bath_full6                                        3.2827e+04  2.8680e+04   1.1446  0.252383    
bath_half1                                        2.8883e+04  1.1610e+03  24.8787 < 2.2e-16 ***
bath_half2                                        5.8058e+04  8.7735e+03   6.6175 3.729e-11 ***
bath_half3                                        5.9695e+04  1.3787e+04   4.3299 1.498e-05 ***
bath_half4                                        6.4025e+04  3.3800e+03  18.9422 < 2.2e-16 ***
bath_half5                                       -3.7625e+04  3.9698e+04  -0.9478  0.343252    
age                                              -1.9340e+03  8.6008e+01 -22.4866 < 2.2e-16 ***
dom                                              -5.2607e+01  5.9970e+00  -8.7722 < 2.2e-16 ***
sold_date                                         6.8952e-01  4.9820e-01   1.3840  0.166363    
sewer_typeseptic                                 -7.5605e+03  1.5115e+03  -5.0021 5.710e-07 ***
sewer_typeunspecified                            -6.8537e+03  7.9062e+02  -8.6688 < 2.2e-16 ***
property_stylenot_mobile                          6.8228e+04  1.7363e+03  39.2941 < 2.2e-16 ***
subdivision1                                      2.6809e+03  9.5201e+02   2.8161  0.004866 ** 
water_typewell                                    2.2071e+03  4.2381e+03   0.5208  0.602522    
waterfront1                                       2.1468e+04  1.5887e+03  13.5129 < 2.2e-16 ***
age_2                                             1.7967e+01  1.1910e+00  15.0862 < 2.2e-16 ***
data_factor$infections_3mma                       1.0638e+01  6.7630e-01  15.7293 < 2.2e-16 ***
bottom25_area_living                             -2.3386e+04  9.1376e+02 -25.5936 < 2.2e-16 ***
data_factor$infections_3mma:bottom25_area_living -3.8844e+00  8.7552e-01  -4.4366 9.178e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


2.6 Corona on Days on Market
2.6.1 Visualization
# Conditional Mean
library(plyr)
dom_mean_data <- ddply(data_factor, "infections_period", summarise, dom_mean = mean(dom, na.rm = TRUE))

# Distribution: Just for City
ggplot(data_factor, aes(x = dom)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    ggtitle("Days on Market Distribution") +
    geom_vline(aes(xintercept = mean(dom)), linetype="dashed", size= 0.4, alpha = 0.5, color = very_low) +
    xlab("Days on Market") +
    ylab("Density")



# Distribution: Infection
ggplot(data_factor, aes(x = dom, fill = infections_period)) +
    geom_density(alpha = 0.5, position = "identity") +
    ggtitle("Days on Market Distributions") +
    geom_vline(data = dom_mean_data, aes(xintercept = dom_mean_data[2,2]), linetype="dashed", size= 0.5, color = med, alpha = 0.8) +
    geom_vline(data = dom_mean_data, aes(xintercept = dom_mean_data[1,2]), linetype="dashed", size= 0.5, alpha = 0.8, color = very_low) +
    xlab("Days on Market") +
    ylab("Density") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))


# Distribution: Top vs. Bottom
ggplot(data_factor) +
    geom_density(aes(x = dom, fill = infections_period), alpha = 0.5, position = "identity") + 
    facet_grid(vars(top25_dom, bottom25_dom), scales = "free") +
    ggtitle("Days on Market Distributions") +
    xlab("Days on Market") +
    ylab("Density") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))


#dom on Infections
ggplot(data_factor, aes(x = infections_period, y = dom, fill = infections_period)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    #coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)) +
    ggtitle("Comparison of Days on Market") +
    xlab("Infection Period") +
    ylab("Days on Market") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

2.6.2 Modeling
coeftest(lm_corona_dom_bottom, vcov = vcovHC(lm_corona_dom_bottom, method = "White2", type = "HC0"))

t test of coefficients:

                                            Estimate  Std. Error  t value  Pr(>|t|)    
(Intercept)                               1.8708e+05  3.2369e+04   5.7796 7.578e-09 ***
ac_typenone                              -4.3502e+04  1.9826e+03 -21.9419 < 2.2e-16 ***
ac_typenot_central                       -1.3181e+04  1.5901e+03  -8.2895 < 2.2e-16 ***
patio1                                    7.7980e+03  7.7727e+02  10.0326 < 2.2e-16 ***
school_general1                           1.0761e+04  1.0337e+03  10.4104 < 2.2e-16 ***
photo_count                               9.9320e+02  4.9250e+01  20.1664 < 2.2e-16 ***
pool1                                     1.0726e+04  1.3912e+03   7.7098 1.307e-14 ***
roof_typeother                            2.7998e+03  1.4391e+03   1.9455 0.0517306 .  
roof_typeshingle                          1.9954e+04  1.6443e+03  12.1353 < 2.2e-16 ***
roof_typeslate                            1.0039e+04  9.9523e+03   1.0087 0.3131423    
gas_typenatural                          -9.0369e+04  3.6267e+03 -24.9175 < 2.2e-16 ***
gas_typenone                             -1.2972e+05  2.5068e+03 -51.7475 < 2.2e-16 ***
gas_typepropane                          -1.0043e+05  1.7888e+04  -5.6147 1.991e-08 ***
gas_typeunknown                          -1.3365e+05  2.4104e+03 -55.4476 < 2.2e-16 ***
out_building1                            -5.1575e+03  8.2715e+02  -6.2352 4.585e-10 ***
area_living                               3.3177e+01  6.1916e+00   5.3584 8.472e-08 ***
land_acres                                3.3123e+03  9.5344e+02   3.4740 0.0005136 ***
appliances1                               2.4473e+04  1.1320e+03  21.6202 < 2.2e-16 ***
garage1                                   1.2334e+04  7.6942e+02  16.0305 < 2.2e-16 ***
property_conditionnew                    -2.3336e+04  6.5133e+03  -3.5829 0.0003405 ***
property_conditionother                  -2.0485e+04  9.4800e+02 -21.6090 < 2.2e-16 ***
energy_efficient1                         1.4336e+04  8.4221e+02  17.0216 < 2.2e-16 ***
exterior_typemetal                       -9.4351e+01  2.3687e+03  -0.0398 0.9682277    
exterior_typeother                        1.2027e+04  1.0753e+03  11.1843 < 2.2e-16 ***
exterior_typevinyl                        5.5264e+03  1.1123e+03   4.9682 6.802e-07 ***
exterior_typewood                         3.7714e+03  1.7809e+03   2.1177 0.0342126 *  
exterior_featurescourtyard                4.0526e+04  1.4388e+04   2.8167 0.0048552 ** 
exterior_featuresfence                   -2.2187e+04  5.4551e+03  -4.0673 4.770e-05 ***
exterior_featuresnone                    -1.6005e+04  5.4667e+03  -2.9277 0.0034174 ** 
exterior_featuresporch                   -2.3004e+04  5.5210e+03  -4.1666 3.103e-05 ***
exterior_featurestennis_court             7.5932e+03  1.0821e+04   0.7017 0.4828551    
fireplace1                                1.1940e+04  8.3389e+02  14.3183 < 2.2e-16 ***
foundation_typeslab                       1.3321e+04  1.2877e+03  10.3450 < 2.2e-16 ***
foundation_typeunspecified                7.8067e+03  1.4283e+03   5.4656 4.658e-08 ***
beds_total1                              -2.9076e+04  2.6956e+04  -1.0786 0.2807572    
beds_total2                              -3.4438e+04  2.6858e+04  -1.2822 0.1997713    
beds_total3                              -3.4970e+04  2.6893e+04  -1.3003 0.1935003    
beds_total4                              -3.1785e+04  2.6925e+04  -1.1805 0.2378043    
beds_total5                              -4.9896e+04  2.7337e+04  -1.8252 0.0679782 .  
bath_full1                               -3.3866e+04  2.3351e+04  -1.4503 0.1469909    
bath_full2                               -1.1397e+04  2.3337e+04  -0.4884 0.6252799    
bath_full3                                1.4332e+04  2.3430e+04   0.6117 0.5407548    
bath_full4                                1.7551e+04  2.9478e+04   0.5954 0.5515762    
bath_full6                               -1.5768e+04  2.4025e+04  -0.6563 0.5116069    
bath_half1                                1.2603e+04  1.1311e+03  11.1419 < 2.2e-16 ***
bath_half2                                3.7982e+04  7.7802e+03   4.8819 1.057e-06 ***
bath_half3                                5.7772e+04  1.2170e+04   4.7472 2.074e-06 ***
bath_half4                                8.2835e+04  3.2808e+03  25.2484 < 2.2e-16 ***
bath_half5                               -5.7385e+04  2.7280e+04  -2.1036 0.0354271 *  
age                                      -2.0364e+03  8.3985e+01 -24.2475 < 2.2e-16 ***
sold_date                                -6.4800e-02  4.7932e-01  -0.1352 0.8924613    
sewer_typeseptic                         -5.7769e+03  1.4590e+03  -3.9596 7.529e-05 ***
sewer_typeunspecified                    -4.6494e+03  7.5422e+02  -6.1646 7.177e-10 ***
property_stylenot_mobile                  6.7806e+04  1.7712e+03  38.2830 < 2.2e-16 ***
subdivision1                              3.5519e+03  9.1933e+02   3.8636 0.0001120 ***
water_typewell                            1.5226e+03  4.0416e+03   0.3767 0.7063762    
waterfront1                               2.0260e+04  1.5043e+03  13.4680 < 2.2e-16 ***
age_2                                     1.8678e+01  1.1788e+00  15.8454 < 2.2e-16 ***
area_living_2                             8.9998e-03  1.7761e-03   5.0672 4.067e-07 ***
data_factor$infections_3mma               1.0360e+01  7.4316e-01  13.9409 < 2.2e-16 ***
bottom25_dom                              1.4593e+04  1.0153e+03  14.3736 < 2.2e-16 ***
data_factor$infections_3mma:bottom25_dom -2.1908e+00  9.2971e-01  -2.3564 0.0184597 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


2.7 Corona on City
2.7.1 Visualization
# Conditional Mean
library(plyr)
city_limits_mean_data <- ddply(subset(data_factor, data_factor$city_limits ==1), "infections_period", summarise, city_limits_mean = mean(sold_price, na.rm = TRUE))

# Distribution: Just City
ggplot(data = subset(data_factor, data_factor$city_limits ==1), aes(x = sold_price)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    ggtitle("Price Distribution of Properties in City Limits") +
    geom_vline(aes(xintercept = mean(city_limits)), linetype="dashed", size= 0.4, alpha = 0.5) +
    xlab("Sold Price") +
    ylab("Density")
Warning in mean.default(city_limits) :
  argument is not numeric or logical: returning NA
Warning: Removed 23399 rows containing missing values (geom_vline).

# Distribution: Infection
ggplot(data = subset(data_factor, data_factor$city_limits ==1), aes(x = sold_price, fill = infections_period)) +
    geom_density(alpha = 0.5, position = "identity") +
    ggtitle("Price Distributions of Properties in City Limits") +
    geom_vline(data = city_limits_mean_data, aes(xintercept = city_limits_mean_data[2,2]), linetype="dashed", size= 0.5, color = med, alpha = 0.8) +
    geom_vline(data = city_limits_mean_data, aes(xintercept = city_limits_mean_data[1,2]), linetype="dashed", size= 0.5, alpha = 0.8, color = very_low) +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post")) +
    xlab("Sold Price") +
    ylab("Density") 


#city_limits on Infections
ggplot(data_factor, aes(x = city_limits, y = sold_price, fill = infections_period)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1, alpha = 0.9) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    #coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)) +
    ggtitle("Comparison of Price: City Limts and Pre vs. Post Corona") +
    xlab("City Limits and Infection Period") +
    ylab("Sold Price") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "City Limits",
                      labels = c("Pre", "Post"))
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

2.7.2 Modeling

# Testing Corona, City Limits
lm_corona_city <- lm(sold_price ~ . 
               
                       # test variable(s)                    
                       + data_factor$infections_3mma + data_factor$city_limits 
                       + data_factor$infections_3mma*data_factor$city_limits
                       
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_city, vcov = vcovHC(lm_corona_city, method = "White2", type = "HC0"))

t test of coefficients:

                                                        Estimate  Std. Error  t value  Pr(>|t|)    
(Intercept)                                           1.7861e+05  3.3976e+04   5.2570 1.477e-07 ***
property_typeDUP                                     -5.2256e+04  1.5467e+04  -3.3785 0.0007300 ***
property_typeOTH                                      2.5209e+04  1.5054e+04   1.6746 0.0940318 .  
property_typePAT                                      1.6422e+04  5.5954e+03   2.9350 0.0033385 ** 
property_typeSGL                                      2.2707e+04  2.7116e+03   8.3742 < 2.2e-16 ***
property_typeTNH                                     -3.4785e+03  3.3351e+03  -1.0430 0.2969503    
ac_typenone                                          -4.5713e+04  1.9689e+03 -23.2178 < 2.2e-16 ***
ac_typenot_central                                   -1.3706e+04  1.5987e+03  -8.5732 < 2.2e-16 ***
patio1                                                8.1712e+03  7.7788e+02  10.5044 < 2.2e-16 ***
school_general1                                       1.1846e+04  1.0460e+03  11.3259 < 2.2e-16 ***
photo_count                                           9.1824e+02  4.8848e+01  18.7978 < 2.2e-16 ***
pool1                                                 1.3109e+04  1.3993e+03   9.3684 < 2.2e-16 ***
roof_typeother                                        3.6223e+03  1.4471e+03   2.5032 0.0123145 *  
roof_typeshingle                                      2.1298e+04  1.6497e+03  12.9106 < 2.2e-16 ***
roof_typeslate                                        1.0056e+04  9.8591e+03   1.0200 0.3077618    
gas_typenatural                                      -8.9629e+04  3.6449e+03 -24.5899 < 2.2e-16 ***
gas_typenone                                         -1.3151e+05  2.4644e+03 -53.3656 < 2.2e-16 ***
gas_typepropane                                      -9.9907e+04  1.8268e+04  -5.4690 4.571e-08 ***
gas_typeunknown                                      -1.3692e+05  2.3453e+03 -58.3818 < 2.2e-16 ***
out_building1                                        -6.1045e+03  8.2702e+02  -7.3814 1.616e-13 ***
area_living                                           3.2060e+01  6.1705e+00   5.1957 2.056e-07 ***
land_acres                                            2.0637e+03  9.4585e+02   2.1819 0.0291294 *  
appliances1                                           2.4475e+04  1.1334e+03  21.5939 < 2.2e-16 ***
garage1                                               1.2014e+04  7.7206e+02  15.5615 < 2.2e-16 ***
property_conditionnew                                -2.1188e+04  6.2676e+03  -3.3805 0.0007246 ***
property_conditionother                              -2.1335e+04  9.5483e+02 -22.3443 < 2.2e-16 ***
energy_efficient1                                     1.3986e+04  8.4013e+02  16.6469 < 2.2e-16 ***
exterior_typemetal                                   -7.3384e+01  2.3631e+03  -0.0311 0.9752273    
exterior_typeother                                    1.1645e+04  1.0751e+03  10.8307 < 2.2e-16 ***
exterior_typevinyl                                    5.0111e+03  1.1136e+03   4.5001 6.823e-06 ***
exterior_typewood                                     3.7778e+03  1.7816e+03   2.1205 0.0339778 *  
exterior_featurescourtyard                            3.3821e+04  1.4091e+04   2.4002 0.0163944 *  
exterior_featuresfence                               -3.1962e+04  5.3284e+03  -5.9984 2.021e-09 ***
exterior_featuresnone                                -2.4953e+04  5.3355e+03  -4.6769 2.928e-06 ***
exterior_featuresporch                               -3.2028e+04  5.3922e+03  -5.9396 2.895e-09 ***
exterior_featurestennis_court                        -5.6576e+02  1.0551e+04  -0.0536 0.9572380    
fireplace1                                            1.1828e+04  8.3361e+02  14.1887 < 2.2e-16 ***
foundation_typeslab                                   1.4938e+04  1.2903e+03  11.5773 < 2.2e-16 ***
foundation_typeunspecified                            8.3762e+03  1.4287e+03   5.8630 4.604e-09 ***
beds_total1                                          -3.0336e+04  2.5401e+04  -1.1943 0.2323774    
beds_total2                                          -3.8930e+04  2.5313e+04  -1.5379 0.1240784    
beds_total3                                          -4.5128e+04  2.5374e+04  -1.7785 0.0753342 .  
beds_total4                                          -4.2724e+04  2.5412e+04  -1.6812 0.0927301 .  
beds_total5                                          -6.0622e+04  2.5853e+04  -2.3449 0.0190400 *  
bath_full1                                           -3.2997e+04  2.5077e+04  -1.3158 0.1882552    
bath_full2                                           -8.1502e+03  2.5069e+04  -0.3251 0.7450976    
bath_full3                                            1.8659e+04  2.5159e+04   0.7416 0.4583086    
bath_full4                                            2.1358e+04  3.1105e+04   0.6866 0.4923183    
bath_full6                                            1.9232e+04  2.5880e+04   0.7431 0.4574071    
bath_half1                                            1.4021e+04  1.1345e+03  12.3586 < 2.2e-16 ***
bath_half2                                            3.8677e+04  7.9272e+03   4.8790 1.073e-06 ***
bath_half3                                            5.8459e+04  1.0835e+04   5.3952 6.909e-08 ***
bath_half4                                            7.1968e+04  3.2187e+03  22.3594 < 2.2e-16 ***
bath_half5                                           -6.1887e+04  2.7837e+04  -2.2232 0.0262144 *  
age                                                  -2.0199e+03  8.4330e+01 -23.9525 < 2.2e-16 ***
dom                                                  -6.2165e+01  5.7948e+00 -10.7278 < 2.2e-16 ***
sold_date                                             3.8776e-01  4.7523e-01   0.8159 0.4145389    
sewer_typeseptic                                     -5.7389e+03  1.4748e+03  -3.8912 1.000e-04 ***
sewer_typeunspecified                                -4.6601e+03  7.5909e+02  -6.1391 8.424e-10 ***
property_stylenot_mobile                              6.8636e+04  1.7654e+03  38.8784 < 2.2e-16 ***
subdivision1                                          3.6139e+03  9.1778e+02   3.9376 8.252e-05 ***
water_typewell                                        5.8505e+03  4.1916e+03   1.3958 0.1627978    
waterfront1                                           2.0355e+04  1.5069e+03  13.5081 < 2.2e-16 ***
age_2                                                 1.8234e+01  1.1843e+00  15.3960 < 2.2e-16 ***
area_living_2                                         9.0448e-03  1.7690e-03   5.1129 3.197e-07 ***
data_factor$infections_3mma                           5.1147e+00  1.6642e+00   3.0733 0.0021194 ** 
data_factor$city_limits1                              7.2944e+03  2.2063e+03   3.3062 0.0009470 ***
data_factor$infections_3mma:data_factor$city_limits1  4.9912e+00  1.6744e+00   2.9809 0.0028764 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1



3. Playground


3.1 Index creation

data_index <- read_excel("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Models/Data/New Data/Index_hardkey.xlsx")
attach(data_index)
The following objects are masked from data_index (pos = 6):

    Date, lma_2m, lma_2m_index, lma_3m, lma_3m_index, lma_4m, lma_4m_index, lma_5m,
    lma_5m_index, log_wappsf, ma_2m, ma_3m, ma_4m, ma_5m, wappsf

The following objects are masked from data_index (pos = 10):

    Date, lma_2m, lma_2m_index, lma_3m, lma_3m_index, lma_4m, lma_4m_index, lma_5m,
    lma_5m_index, log_wappsf, ma_2m, ma_3m, ma_4m, ma_5m, wappsf

The following objects are masked from data_index (pos = 14):

    Date, lma_2m, lma_2m_index, lma_3m, lma_3m_index, lma_4m, lma_4m_index, lma_5m,
    lma_5m_index, log_wappsf, ma_2m, ma_3m, ma_4m, ma_5m, wappsf

The following objects are masked from data_index (pos = 17):

    Date, lma_2m, lma_2m_index, lma_3m, lma_3m_index, lma_4m, lma_4m_index, lma_5m,
    lma_5m_index, log_wappsf, ma_2m, ma_3m, ma_4m, ma_5m, wappsf

The following objects are masked from data_index (pos = 23):

    Date, lma_2m, lma_2m_index, lma_3m, lma_3m_index, lma_4m, lma_4m_index, lma_5m,
    lma_5m_index, log_wappsf, ma_2m, ma_3m, ma_4m, ma_5m, wappsf

The following objects are masked from data_index (pos = 27):

    Date, lma_2m, lma_2m_index, lma_3m, lma_3m_index, lma_4m, lma_4m_index, lma_5m,
    lma_5m_index, log_wappsf, ma_2m, ma_3m, ma_4m, ma_5m, wappsf

The following objects are masked from data_index (pos = 31):

    Date, lma_2m, lma_2m_index, lma_3m, lma_3m_index, lma_4m, lma_4m_index, lma_5m,
    lma_5m_index, log_wappsf, ma_2m, ma_3m, ma_4m, ma_5m, wappsf

The following objects are masked from data_index (pos = 35):

    Date, lma_2m, lma_2m_index, lma_3m, lma_3m_index, lma_4m, lma_4m_index, lma_5m,
    lma_5m_index, log_wappsf, ma_2m, ma_3m, ma_4m, ma_5m, wappsf

The following objects are masked from data_index (pos = 39):

    Date, lma_2m, lma_2m_index, lma_3m, lma_3m_index, lma_4m, lma_4m_index, lma_5m,
    lma_5m_index, log_wappsf, ma_2m, ma_3m, ma_4m, ma_5m, wappsf
data_index_fred <- read_excel("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Models/Data/New Data/Index_FRED.xls")
attach(data_index_fred)
The following object is masked from data_index_fred_1975_total (pos = 4):

    date

The following object is masked from data_index_gdp (pos = 5):

    date

The following objects are masked from data_index_fred (pos = 6):

    date, index_Q1_1980

The following object is masked from data_index_fred_1975_total (pos = 8):

    date

The following object is masked from data_index_gdp (pos = 9):

    date

The following objects are masked from data_index_fred (pos = 10):

    date, index_Q1_1980

The following object is masked from data_index_fred_1975_total (pos = 12):

    date

The following object is masked from data_index_gdp (pos = 13):

    date

The following objects are masked from data_index_fred (pos = 14):

    date, index_Q1_1980

The following object is masked from data_index_gdp (pos = 16):

    date

The following objects are masked from data_index_fred (pos = 17):

    date, index_Q1_1980

The following object is masked from data_index_fred_1975_total (pos = 19):

    date

The following object is masked from data_index_fred_1975_total (pos = 20):

    date

The following object is masked from data_index_gdp (pos = 22):

    date

The following objects are masked from data_index_fred (pos = 23):

    date, index_Q1_1980

The following object is masked from data_index_gdp (pos = 26):

    date

The following objects are masked from data_index_fred (pos = 27):

    date, index_Q1_1980

The following object is masked from data_index_gdp (pos = 30):

    date

The following objects are masked from data_index_fred (pos = 31):

    date, index_Q1_1980

The following object is masked from data_index_gdp (pos = 34):

    date

The following objects are masked from data_index_fred (pos = 35):

    date, index_Q1_1980

The following object is masked from data_index_gdp (pos = 38):

    date

The following objects are masked from data_index_fred (pos = 39):

    date, index_Q1_1980
data_index_gdp <- read_excel("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Models/Data/New Data/la_GDP.xls")
attach(data_index_gdp)
The following object is masked from data_index_fred (pos = 3):

    date

The following object is masked from data_index_fred_1975_total (pos = 5):

    date

The following objects are masked from data_index_gdp (pos = 6):

    date, real_gdp, real_gdp_Index, real_gdp_re_specific, real_gdp_re_specific_index

The following object is masked from data_index_fred (pos = 7):

    date

The following object is masked from data_index_fred_1975_total (pos = 9):

    date

The following objects are masked from data_index_gdp (pos = 10):

    date, real_gdp, real_gdp_Index, real_gdp_re_specific, real_gdp_re_specific_index

The following object is masked from data_index_fred (pos = 11):

    date

The following object is masked from data_index_fred_1975_total (pos = 13):

    date

The following objects are masked from data_index_gdp (pos = 14):

    date, real_gdp, real_gdp_Index, real_gdp_re_specific, real_gdp_re_specific_index

The following object is masked from data_index_fred (pos = 15):

    date

The following objects are masked from data_index_gdp (pos = 17):

    date, real_gdp, real_gdp_Index, real_gdp_re_specific, real_gdp_re_specific_index

The following object is masked from data_index_fred (pos = 18):

    date

The following object is masked from data_index_fred_1975_total (pos = 20):

    date

The following object is masked from data_index_fred_1975_total (pos = 21):

    date

The following objects are masked from data_index_gdp (pos = 23):

    date, real_gdp, real_gdp_Index, real_gdp_re_specific, real_gdp_re_specific_index

The following object is masked from data_index_fred (pos = 24):

    date

The following objects are masked from data_index_gdp (pos = 27):

    date, real_gdp, real_gdp_Index, real_gdp_re_specific, real_gdp_re_specific_index

The following object is masked from data_index_fred (pos = 28):

    date

The following objects are masked from data_index_gdp (pos = 31):

    date, real_gdp, real_gdp_Index, real_gdp_re_specific, real_gdp_re_specific_index

The following object is masked from data_index_fred (pos = 32):

    date

The following objects are masked from data_index_gdp (pos = 35):

    date, real_gdp, real_gdp_Index, real_gdp_re_specific, real_gdp_re_specific_index

The following object is masked from data_index_fred (pos = 36):

    date

The following objects are masked from data_index_gdp (pos = 39):

    date, real_gdp, real_gdp_Index, real_gdp_re_specific, real_gdp_re_specific_index

The following object is masked from data_index_fred (pos = 40):

    date
data_index_fred_1975_total <- read_excel("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Models/Data/New Data/Total_US_1975.xls")
attach(data_index_fred_1975_total)
The following object is masked from data_index_gdp (pos = 3):

    date

The following object is masked from data_index_fred (pos = 4):

    date

The following objects are masked from data_index_fred_1975_total (pos = 6):

    date, gdp_pc_nom_index_1975, gdp_pc_real_index_1975, re_cpi, re_nom_index_1975,
    re_real_index_1975

The following object is masked from data_index_gdp (pos = 7):

    date

The following object is masked from data_index_fred (pos = 8):

    date

The following objects are masked from data_index_fred_1975_total (pos = 10):

    date, gdp_pc_nom_index_1975, gdp_pc_real_index_1975, re_cpi, re_nom_index_1975,
    re_real_index_1975

The following object is masked from data_index_gdp (pos = 11):

    date

The following object is masked from data_index_fred (pos = 12):

    date

The following objects are masked from data_index_fred_1975_total (pos = 14):

    date, gdp_pc_nom_index_1975, gdp_pc_real_index_1975, re_cpi, re_nom_index_1975,
    re_real_index_1975

The following object is masked from data_index_gdp (pos = 15):

    date

The following object is masked from data_index_fred (pos = 16):

    date

The following object is masked from data_index_gdp (pos = 18):

    date

The following object is masked from data_index_fred (pos = 19):

    date

The following objects are masked from data_index_fred_1975_total (pos = 21):

    date, gdp_pc_nom_index_1975, gdp_pc_real_index_1975, re_cpi, re_nom_index_1975,
    re_real_index_1975

The following object is masked from data_index_fred_1975_total (pos = 22):

    date

The following object is masked from data_index_gdp (pos = 24):

    date

The following object is masked from data_index_fred (pos = 25):

    date

The following object is masked from data_index_gdp (pos = 28):

    date

The following object is masked from data_index_fred (pos = 29):

    date

The following object is masked from data_index_gdp (pos = 32):

    date

The following object is masked from data_index_fred (pos = 33):

    date

The following object is masked from data_index_gdp (pos = 36):

    date

The following object is masked from data_index_fred (pos = 37):

    date

The following object is masked from data_index_gdp (pos = 40):

    date

The following object is masked from data_index_fred (pos = 41):

    date
# Index graphing
ggplot(data_index, aes(x = Date)) +
    geom_line(mapping = aes(y = lma_2m_index), color = "darkred") +
    geom_line(mapping = aes(y = lma_3m_index), color = "darkgreen") +
    geom_line(mapping = aes(y = lma_4m_index), color = "darkblue") +
    geom_line(mapping = aes(y = lma_5m_index), color = "grey45") +
    geom_vline(xintercept = as.numeric(as.Date("2020-03-23")), linetype=4, color = "green") +
    #scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    scale_y_continuous(limits = c(min(lma_2m_index),max(lma_2m_index))) +
    xlab(" ") +
    ylab("Weighted Average Price per Sqft.") +
    labs(title = "Louisiana Housing Index",
         caption = "") 


# FRED quarterly data
ggplot(data_index_fred, aes(x = date)) + 
    geom_line(aes(y = index_Q1_1980), color = "darkred") +
    theme_minimal() +
    geom_vline(xintercept = as.Date("2020-01-01"), linetype=4, color = "green") +
    #scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    scale_y_continuous(limits = c(min(index_Q1_1980),max(index_Q1_1980))) +
    xlab(" ") +
    ylab("Index Value") +
    labs(title = "Louisiana Housing Index: FRED St. Louis",
         caption = "") 


# La Real GDP data quarterly data
data_index_gdp <- subset(data_index_gdp, data_index_gdp$date >= as.Date("2011-07-01"))
ggplot(data_index_gdp, aes(x = date)) + 
    geom_line(aes(y = real_gdp_Index, color = "darkred"), linetype = "dashed", size = .5) +
    geom_line(aes(y = real_gdp_re_specific_index, color = "darkblue"), size = .5) +
    theme(legend.position = "bottom") +
    geom_vline(xintercept = as.Date("2020-01-01"), linetype = 4, color = "green") +
    #scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    #scale_y_continuous(limits = c(min(real_gdp_Index),max(real_gdp_Index))) +
    xlab(" ") +
    ylab("Index Value") +
    labs(title = "Louisiana GDP and Housing Index: FRED St. Louis",
         caption = "") +
         scale_color_discrete(name = "Infection Period",
                              labels = c("RE Index", "Aggrigate GDP Index"))


cor.test(real_gdp_Index, real_gdp_re_specific_index)

    Pearson's product-moment correlation

data:  real_gdp_Index and real_gdp_re_specific_index
t = -2.0521, df = 65, p-value = 0.04419
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.459638677 -0.006861981
sample estimates:
      cor 
-0.246664 
# TOTAL US Real GDP data quarterly data base 2011
ggplot(data_index_fred_total, aes(x = observation_date)) + 
    geom_line(aes(y = GDP, color = very_low), linetype = "dashed", size = .5) +
    geom_line(aes(y = all_re_index, color = med), size = .5) +
    theme(legend.position = "bottom" ) +
    geom_vline(xintercept = as.Date("2020-01-01"), linetype=4, color = "green") +
    #scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    #scale_y_continuous(limits = c(min(real_gdp_Index),max(real_gdp_Index))) +
    xlab(" ") +
    ylab("Index Value") +
    labs(title = "Total US GDP and Housing Index: FRED St. Louis",
         caption = "") +
         scale_color_discrete(name = "Infection Period",
                              labels = c("RE Index", "Aggrigate GDP Index"))

  
                    
# TOTAL US Real GDP data quarterly data base 1975

# Nominal
ggplot(data_index_fred_1975_total, aes(x = date)) + 
    geom_line(aes(y = gdp_pc_nom_index_1975 , color = very_low), linetype = "dashed", size = .5) +
    geom_line(aes(y = re_nom_index_1975, color = med), size = .5) +
    theme(legend.position = "bottom" ) +
    #geom_vline(xintercept = as.Date("2020-01-01"), linetype=4, color = "green") +
    #scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    #scale_y_continuous(limits = c(min(real_gdp_Index),max(real_gdp_Index))) +
    xlab(" ") +
    ylab("Index Value (1975 Q1 = 100)") +
    labs(title = "U.S. GDP and Housing Index",
         caption = "FRED, St. Louis") +
         scale_color_discrete(name = "",
                              labels = c("Nominal Housing Prices", "Nominal GDP Per-Capita"))


corr_nom_1975 <- cor(gdp_pc_nom_index_1975, re_nom_index_1975)


# Real
ggplot(data_index_fred_1975_total, aes(x = date)) + 
    geom_line(aes(y = gdp_pc_real_index_1975 , color = very_low), linetype = "dashed", size = .5) +
    geom_line(aes(y = re_real_index_1975, color = med), size = .5) +
    theme(legend.position = "bottom" ) +
    #geom_vline(xintercept = as.Date("2020-01-01"), linetype=4, color = "green") +
    #scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    #scale_y_continuous(limits = c(min(real_gdp_Index),max(real_gdp_Index))) +
    xlab(" ") +
    ylab("Index Value (1975 Q1 = 100)") +
    labs(title = "U.S. GDP and Housing Index",
         caption = "FRED, St. Louis") +
         scale_color_discrete(name = "",
                              labels = c("Real Housing Prices", "Real GDP Per-Capita"))


corr_real_1975 <- cor(gdp_pc_real_index_1975, re_real_index_1975)

corr_nom_1975
[1] 0.9827026
corr_real_1975
[1] 0.6235355
3.2 Playing with Maps
# packages
require(ggplot2)
install.packages("ggmap")
require(maps)
install.packages(Geoc)



#Basic Map
LA <- map_data("state", region="louisiana")
ggplot(LA, aes(x=long, y=lat))+geom_polygon()


# data
salesCalls <- data.frame(State=rep("louisiana",5), 
                             City=c("Baton Rouge","New Orleans", "Shreveport",       "Lafayette", "Mandeville"),
                             Calls=c(10,5,8,13,2))

salesCalls <- cbind(geocode(as.character(salesCalls$City)), salesCalls)



?cbind

ggplot(LA, aes(x=long, y=lat)) +
  geom_polygon() +
  coord_map() +
  geom_point(data=salesCalls, aes(x=lon, y=lat, size=Calls), color="orange")
3.3 Reduction in Dimensionality
library(boot) # K-fold
library(leaps) # Subset 
library(glmnet) #glmnet() is the main function in the glmnet package (must pass in an x matrix as well as a y vector)

# Set x-y definitions for glmnet package 
x <- model.matrix(sold_price ~ . ,data = data_factor_core_clean)[, -1]

y <- data_factor_core_clean$sold_price[1:24653] # Manually restricted due rows not matching with x 'x' for an unknown reason

# General grid
grid <- exp(seq(10, -65, length = 101)) #grid of values from exp(10) [null model] to exp(-15) [least squares]

#Lasso
set.seed(1)
cv.out <- cv.glmnet(x, y, alpha = 1, lambda = grid, nfolds = 10) #lasso
plot(cv.out)

# Base decision
bestlam <- cv.out$lambda.min; bestlam; log(bestlam)
out <- cv.out$glmnet.fit
lasso.coef <- predict(out, type = "coefficients", s = bestlam); lasso.coef; lasso.coef[lasso.coef != 0]
sum(abs(lasso.coef[1:31])) #l1 norm

# +1se decision
bestlam2 <- cv.out$lambda.1se; bestlam2; log(bestlam2)
lasso.coef2 <- predict(out, type = "coefficients", s = bestlam2); lasso.coef2; lasso.coef2[lasso.coef2 != 0]
sum(abs(lasso.coef2[2:31])) #l1 norm
3.4 Basic 3D Graphing
kd <- with(MASS::geyser, MASS::kde2d(sold_price, infections_3mma, n = 50))

fig <- plot_ly(x = kd$x, y = kd$y, z = kd$z) %>% add_surface()

fig
3.4 Descriptive Stats

# Distribution: Total
a <- ggplot(data_factor, aes(x = sold_price/1000)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Sold Price") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

b <- ggplot(data_factor, aes(x = list_price/1000)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("List Price") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 


c <- ggplot(data_factor, aes(x = area_living)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Living Area") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

d <- ggplot(data_factor, aes(x = land_acres)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Land in Acres") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

e <- ggplot(data_factor, aes(x = area_total)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Total Area") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

f <- ggplot(data_factor, aes(x = age)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Age") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

g <- ggplot(data_factor, aes(x = dom)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("DOM") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

data_factor$sold_date <- as.Date(data_factor$sold_date)
str(data_factor)
tibble [24,412 × 49] (S3: tbl_df/tbl/data.frame)
 $ mls_number          : chr [1:24412] "CNNN5274" "CNNN5241" "CNN104918" "CNN104870" ...
 $ property_type       : Factor w/ 6 levels "CND","DUP","OTH",..: 5 5 5 5 5 5 5 5 5 5 ...
 $ ac_type             : Factor w/ 3 levels "central","none",..: 1 3 1 1 1 1 1 1 1 1 ...
 $ list_price          : num [1:24412] 187000 250000 224900 225000 274900 ...
 $ patio               : Factor w/ 2 levels "0","1": 1 1 1 2 2 1 2 2 2 2 ...
 $ school_general      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ photo_count         : num [1:24412] 0 0 0 0 25 2 6 17 17 15 ...
 $ pool                : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 1 2 1 ...
 $ roof_type           : Factor w/ 4 levels "metal","other",..: 3 3 2 3 3 3 2 3 2 2 ...
 $ gas_type            : Factor w/ 5 levels "butane","natural",..: 5 5 5 5 5 5 5 5 5 5 ...
 $ out_building        : Factor w/ 2 levels "0","1": 1 1 2 1 1 1 1 1 2 1 ...
 $ area_living         : num [1:24412] 2054 2120 2078 1923 2184 ...
 $ land_acres          : num [1:24412] 0.28 0.4 0.29 0.36 0.82 0.36 1 1.27 0.63 2.01 ...
 $ appliances          : Factor w/ 2 levels "0","1": 2 1 2 2 2 2 2 2 2 2 ...
 $ garage              : Factor w/ 2 levels "0","1": 2 2 1 2 2 2 2 2 2 2 ...
 $ property_condition  : Factor w/ 3 levels "excellent","new",..: 3 3 3 3 3 3 3 3 3 3 ...
 $ energy_efficient    : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 2 1 2 ...
 $ exterior_type       : Factor w/ 5 levels "brick","metal",..: 3 3 4 4 1 3 4 1 3 3 ...
 $ exterior_features   : Factor w/ 6 levels "balcony","courtyard",..: 4 4 4 5 5 3 4 5 3 3 ...
 $ fireplace           : Factor w/ 2 levels "0","1": 2 1 1 2 2 2 2 2 2 2 ...
 $ foundation_type     : Factor w/ 3 levels "raised","slab",..: 1 2 1 2 2 3 2 2 2 2 ...
 $ area_total          : num [1:24412] 2254 2120 2962 2550 3510 ...
 $ beds_total          : Factor w/ 6 levels "0","1","2","3",..: 4 5 4 4 4 4 5 4 5 5 ...
 $ bath_full           : Factor w/ 6 levels "0","1","2","3",..: 3 3 3 3 3 3 3 3 3 3 ...
 $ bath_half           : Factor w/ 6 levels "0","1","2","3",..: 1 1 1 2 1 1 1 1 2 1 ...
 $ age                 : num [1:24412] 82 9 70 27 7 6 38 32 15 5 ...
 $ dom                 : num [1:24412] 78 83 89 203 231 54 144 108 26 25 ...
 $ sold_price          : num [1:24412] 169000 245000 230000 220000 272000 ...
 $ sold_date           : Date[1:24412], format: "2016-02-12" "2016-11-18" "2017-03-03" "2017-06-19" ...
 $ sewer_type          : Factor w/ 3 levels "city","septic",..: 1 1 1 2 2 1 3 3 1 3 ...
 $ property_style      : Factor w/ 2 levels "mobile","not_mobile": 2 2 2 2 2 2 2 2 2 2 ...
 $ city_limits         : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
 $ subdivision         : Factor w/ 2 levels "0","1": 2 1 2 2 2 2 2 2 2 2 ...
 $ water_type          : Factor w/ 2 levels "public","well": 1 1 1 1 1 1 1 1 1 1 ...
 $ waterfront          : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ...
 $ infections_daily    : num [1:24412] 0 0 0 0 0 0 0 0 0 0 ...
 $ infections_accum    : num [1:24412] 0 0 0 0 0 0 0 0 0 0 ...
 $ corona_date_split   : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ infections_3mma     : num [1:24412] 0 0 0 0 0 0 0 0 0 0 ...
 $ top25_sold_price    : Factor w/ 2 levels "0","1": 1 2 1 1 2 2 1 2 2 2 ...
 $ top50_sold_price    : num [1:24412] 0 1 1 1 1 1 1 1 1 1 ...
 $ bottom25_sold_price : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ top25_area_living   : Factor w/ 2 levels "0","1": 2 2 2 1 2 2 2 2 2 1 ...
 $ bottom25_area_living: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ top25_age           : Factor w/ 2 levels "0","1": 2 1 2 1 1 1 1 1 1 1 ...
 $ bottom25_age        : Factor w/ 2 levels "0","1": 1 2 1 1 2 2 1 1 2 2 ...
 $ top25_dom           : Factor w/ 2 levels "0","1": 1 1 1 2 2 1 2 1 1 1 ...
 $ bottom25_dom        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
 $ infections_period   : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
h <- ggplot(data_factor, aes(x = sold_date)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Sold Date") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) +
    scale_x_date(date_labels = "%Y")

i <- ggplot(data = subset(data_factor, data_factor$infections_daily > 1), aes(x = infections_daily)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Infections Daily") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

data_factor$beds_total <- as.numeric(data_factor$beds_total)
j <- ggplot(data_factor, aes(x=beds_total)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    scale_fill_manual(values=c(very_low)) +
    xlab("Number of Bedrooms") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

data_factor$bath_full <- as.numeric(data_factor$bath_full)
k <- ggplot(data_factor, aes(x=bath_full)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    scale_fill_manual(values=c(very_low)) +
    xlab("Number of Full Bathrooms") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

data_factor$bath_half <- as.numeric(data_factor$bath_half)
l <- ggplot(data_factor, aes(x=bath_half)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    scale_fill_manual(values=c(very_low)) +
    xlab("Number of Half Bathrooms") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

gridExtra::grid.arrange(a,b,c,d,e,f,g,h,i,j,k,l, nrow =4, ncol = 3)
Warning: Removed 16 rows containing non-finite values (stat_density).
Warning: Removed 2 rows containing non-finite values (stat_density).
<<<<<<< HEAD

=======

>>>>>>> cd3d4497875c198536d12af185cf61114c92970a
3.4 Simple UCLA Case
lm_ucla <- lm(sold_price ~ pool + infections_period + pool*infections_period, data = data_factor)
summ(lm_ucla)
MODEL INFO:
Observations: 24412
Dependent Variable: sold_price
Type: OLS linear regression 

MODEL FIT:
F(3,24408) = 635.35, p = 0.00
R² = 0.07
Adj. R² = 0.07 

Standard errors: OLS
--------------------------------------------------------------------
                                      Est.      S.E.   t val.      p
------------------------------ ----------- --------- -------- ------
(Intercept)                      154123.97    636.84   242.02   0.00
pool1                             53118.17   2271.44    23.39   0.00
infections_period1                41724.75   1258.93    33.14   0.00
pool1:infections_period1          -7766.40   4259.65    -1.82   0.07
--------------------------------------------------------------------
# load package
library(sjPlot)
library(sjmisc)
library(sjlabelled)

tab_model(lm_ucla)

end of document

<<<<<<< HEAD
---
title: "Hedonic Pricing Models"
output:
  html_notebook: default
  pdf_document: default
  word_document: default
code_folding: hide
Author: Sawyer Benson
---

### Sawyer Benson's Master Thesis 
### Janurary 10, 2022


```{r message=TRUE, warning=TRUE, include=FALSE, results='hide'}

#Read in packages and data

library(readxl) # Import excel data frames
library(ggplot2) # Graphs
library(scales) # Scale range of ggplots 
library(ggfortify) # Additional ggplot2 functionality
library(olsrr) # Testing for heteroscedasticity
library(lmtest) # Testing for heteroscedasticity using breuch-pagan
library(sandwich) # Amending heteroskedasticity 
library(mcvis) # Visualizing multicollinearity
library(gridExtra) # Organize graphs
library(dplyr) # data_factor wrangling
library(tidyr) # data_factor wrangling
library(tinytex) #for RMarkdown
library(openxlsx) #Export data frame into Excel
library(ggeffects) # plotting marginal effects
library(sjPlot) # plotting marginal effects
library(stargazer) # Showing several outputs next to each other in a STATA style
library(modelsummary) # Showing several outputs next to each other in a STATA style
library(regclass) # for testing multicollinearity using VIF
library(jtools) # cleaner regression output (e.g. summ(lm) 
library(tidyverse) # data cleaning
library(hrbrthemes) # special boxplots
library(viridis) # special boxplots
library(plotly) # For 3D plotting in ggplot2

# Import and attach data sets
data_factor <- read_excel("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Models/Data/New Data/3. data_factor_cleaned.xlsx")
attach(data_factor)

# Convert Char to Factors with N Levels
# Structure Change
data_factor$property_type <- as.factor(data_factor$property_type)
data_factor$ac_type <- as.factor(data_factor$ac_type)
data_factor$patio <- as.factor(data_factor$patio)
data_factor$school_general <- as.factor(data_factor$school_general)
data_factor$pool <- as.factor(data_factor$pool)
data_factor$roof_type <- as.factor(data_factor$roof_type)
data_factor$gas_type <- as.factor(data_factor$gas_type)
data_factor$out_building <- as.factor(data_factor$out_building)
data_factor$appliances <- as.factor(data_factor$appliances)
data_factor$garage <- as.factor(data_factor$garage)
data_factor$property_condition <- as.factor(data_factor$property_condition)
data_factor$energy_efficient <- as.factor(data_factor$energy_efficient)
data_factor$exterior_type <- as.factor(data_factor$exterior_type)
data_factor$exterior_features <- as.factor(data_factor$exterior_features)
data_factor$fireplace <- as.factor(data_factor$fireplace)
data_factor$foundation_type <- as.factor(data_factor$foundation_type)
data_factor$beds_total <- as.factor(data_factor$beds_total)
data_factor$bath_full <- as.factor(data_factor$bath_full)
data_factor$bath_half <- as.factor(data_factor$bath_half)
data_factor$sewer_type <- as.factor(data_factor$sewer_type)
data_factor$property_style <- as.factor(data_factor$property_style)
data_factor$subdivision <- as.factor(data_factor$subdivision)
data_factor$water_type <- as.factor(data_factor$water_type)
data_factor$waterfront <- as.factor(data_factor$waterfront)
data_factor$sold_date <- openxlsx::convertToDate(data_factor$sold_date)
data_factor$sold_date <- as.numeric(data_factor$sold_date)

str(data_factor)

# Splits
data_factor$city_limits <- as.factor(data_factor$city_limits)
data_factor$corona_date_split <- as.factor(data_factor$corona_date_split)
data_factor$top25_sold_price <- as.factor(data_factor$top25_sold_price)
data_factor$bottom25_sold_price <- as.factor(data_factor$bottom25_sold_price)
data_factor$top25_area_living <- as.factor(data_factor$top25_area_living)
data_factor$bottom25_area_living  <- as.factor(data_factor$bottom25_area_living)
data_factor$top25_age <- as.factor(data_factor$top25_age)
data_factor$bottom25_age <- as.factor(data_factor$bottom25_age)
data_factor$top25_dom <- as.factor(data_factor$top25_dom)
data_factor$bottom25_dom <- as.factor(data_factor$bottom25_dom)
data_factor$infections_period <- as.numeric(data_factor$infections_accum > 1000)
data_factor$infections_period <- as.factor(data_factor$infections_period)

str(data_factor)

# Remove this weird '20' level is bath_full
levels(data_factor$bath_full)
is.na(data_factor$bath_full) <- data_factor$bath_full == "20"
data_factor$bath_full <- factor(data_factor$bath_full)
levels(data_factor$bath_full)

# Remove beds_total > 5
levels(data_factor$beds_total)
is.na(data_factor$beds_total) <- data_factor$beds_total == "7" 
data_factor$beds_total <- factor(data_factor$beds_total)
is.na(data_factor$beds_total) <- data_factor$beds_total == "6" 
data_factor$beds_total <- factor(data_factor$beds_total)
levels(data_factor$beds_total)



levels(data_factor$beds_total)
levels(data_factor$bath_full)
levels(data_factor$bath_half)

# Data frame without Split Vars
names(data_factor)
data_factor_core <- data_factor[-c(36:47)]
data_factor_core <- subset(data_factor_core, select = -c(city_limits, mls_number, infections_period))
str(data_factor_core)
names(data_factor_core)


```


```{r include=FALSE}

# RMarkdown Code: Format chunk output into scroll lists
# Installed to limit the length of regression output
# save the built-in output hook
hook_output <- knitr::knit_hooks$get("output")

# set a new output hook to truncate text output
knitr::knit_hooks$set(output = function(x, options) {
  if (!is.null(n <- options$out.lines)) {
    x <- xfun::split_lines(x)
    if (length(x) > n) {
      # truncate the output
      x <- c(head(x, n), "....\n")
    }
    x <- paste(x, collapse = "\n")
  }
  hook_output(x, options)
})
``` 

### 1. Model Design: Checks & Corrections

#### 1.1 Accounting for Heteroskedasticity
```{r echo=TRUE, warning=FALSE, attr.output='style="max-height: 250px;"'}

# All-inclusive model
lm_pre_alpha <- lm(sold_price ~ . , data = data_factor_core)
summ(lm_pre_alpha)

# pre_alphaing for heteroskedasticity
#  a. Graphically
par(mfrow = c(2,2))
plot(lm_pre_alpha)

#autoplot(lm_pre_alpha)

#  b. Statistically
ols_test_breusch_pagan(lm_pre_alpha) # Breusch-Pagan test

# - Resolving Heteroskedasticity using heteroskedasticity-consistent (HC) variance covariance matrix

# Compare models
stargazer(lm_pre_alpha,
          coeftest(lm_pre_alpha, vcov = vcovHC(lm_pre_alpha, method = "White2", type = "HC0")),
          coeftest(lm_pre_alpha, vcov = vcovHC(lm_pre_alpha, method = "White2", type = "HC1")),
          type = "text")


```

<br>

#### 1.2 Accounting for Interactions

**Note:** Advisor suggested not to inlude interaction terms except for specific testing.
```{r eval=FALSE, include=FALSE}
#data_binary_test <- data_binary[1:20]
#data_binary_test$sold_price <- data_binary$sold_price

#data_binary_test <- data_binary[1:15]

#lm_check_binary <- lm(data_binary$sold_price ~ ., data = data_binary_test)

#(start.time <- Sys.time())
#lm_check_binary_interactions <- lm(data_binary$sold_price ~ .^2, data = data_binary_test)
#(end.time <- Sys.time())
#(time.taken <- end.time - start.time)

#options(max.print=1000000)
#summary(lm_check)

#94^2 # Number of interactions checked

# Isolating only the interaction which are statistically significant

# 1. Create Boolean vector
#toselect_x <- summary(lm_check_binary_interactions)$coeff[-1,4] < 0.1

# 2. select sig. variables
#relevant_x <- names(toselect_x)[toselect_x == TRUE]
# PROBLEM: interaction are being name with a '1' at the end and that is fucking up the indexing for the last equation.
#(relevant_x <- sub("1", "", relevant_x))

# 3. formula with only sig. variables
#(sig_formula <- as.formula(paste("data_binary$sold_price ~",paste(relevant_x, collapse = "+"))))

# sig_model <- lm(formula = sig_formula, data_binary_test)
# summary(sig_model)

# Compare models
#summ(lm_check_binary, robust = "HC1")
#summ(lm_check_binary_interactions, robust = "HC1")
#summ(sig_model, robust = "HC1")
#stargazer(coeftest(lm_check, vcov = vcovHC(lm_check, method = "White2", type = "HC1")),
#          coeftest(lm_check_binary_interactions, vcov = vcovHC(lm_check_binary_interactions, method = "White2", type = "HC1")),
#          coeftest(sig_model, vcov = vcovHC(sig_model, method = "White2", type = "HC1")),
#         type = "text")
```

<br>

#### 1.3 Accounting for Non-linearity

##### 1.3.1 Age
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# Age
a <- ggplot(data_factor, aes(x = age , y = sold_price)) + 
    geom_point(aes(color = infections_period), alpha = 0.15) + 
    geom_smooth(aes(color = infections_period)) +
    geom_smooth(color = "grey50", linetype = "dashed") +
    theme_minimal()

# Actual vs. fit

# Model with non-linear addition
lm_pre_alpha_age <- lm(sold_price ~ . + I(age^2), data = data_factor_core)
summ(lm_pre_alpha_age)

# Marginal effects data frames
ggpredict_1 <- ggpredict(lm_pre_alpha, terms = "age")
ggpredict_2 <- ggpredict(lm_pre_alpha_age, terms = "age")

# Plots
b <- ggplot(data_factor_core, aes( x = age)) +
   geom_smooth(data_factor_core, mapping = aes(y = sold_price), color = "grey50") +
   geom_smooth(ggpredict_1, mapping = aes(x, predicted), linetype = "dashed", color = "darkred") +
   geom_smooth(ggpredict_2, mapping = aes(x, predicted), linetype = "dashed", color = "darkblue")

# Look at age & age^2 alone to see impact on more relevant y-axis scale
c <- ggplot() +
   geom_smooth(ggpredict_1, mapping = aes(x, predicted), linetype = "dashed", color = "darkred") +
   geom_smooth(ggpredict_2, mapping = aes(x, predicted), linetype = "dashed", color = "darkblue") 

a
gridExtra::grid.arrange(b,c, nrow =2, ncol = 1)

```

<br>

##### 1.3.2 Living Area
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# Living Area

# General graphing
a <- ggplot(data_factor, aes(x = area_living , y = sold_price)) + 
    geom_point(aes(color = infections_period), alpha = 0.15) + 
    geom_smooth(aes(color = infections_period)) +
    geom_smooth(color = "grey50", linetype = "dashed") +
    theme_minimal()

ggplot(data_factor, aes(x = area_living , y = sold_price/area_living)) + 
    geom_point(aes(color = infections_period), alpha = 0.15) + 
    geom_smooth(aes(color = infections_period)) +
    geom_smooth(color = "grey50", linetype = "dashed") +
    theme_minimal()

# Actual vs. fit
# Model with non-linear addition
lm_pre_alpha_area <- lm(sold_price ~ . + I(area_living^2), data = data_factor_core)
summ(lm_pre_alpha_area)

# Model with single-variable fit
lm_pre_alpha_area_single <- lm(sold_price ~ area_living, data = data_factor_core)
summ(lm_pre_alpha_area_single)

# Marginal effects data frames
ggpredict_1 <- ggpredict(lm_pre_alpha, terms = "area_living") # total model
ggpredict_2 <- ggpredict(lm_pre_alpha_area, terms = "area_living") # non-linear addition
ggpredict_3 <- ggpredict(lm_pre_alpha_area_single, terms = "area_living") # single-variable fit

# Plots
b <- ggplot(data_factor_core, aes(x = area_living)) +
   geom_smooth(data_factor, mapping = aes(y = sold_price), color = "grey50") +
   geom_smooth(ggpredict_1, mapping = aes(x, predicted), linetype = "dashed", color = "darkred") +
   geom_smooth(ggpredict_2, mapping = aes(x, predicted), linetype = "dashed", color = "darkblue") +
   geom_smooth(ggpredict_3, mapping = aes(x, predicted), linetype = "dashed", color = "darkblue")

# Look at age & age^2 alone to see impact on more relevant y-axis scale
c <- ggplot() +
   geom_smooth(ggpredict_1, mapping = aes(x, predicted), linetype = "dashed", color = "darkred") +
   geom_smooth(ggpredict_2, mapping = aes(x, predicted), linetype = "dashed", color = "darkblue") 

# Conclusion
a
gridExtra::grid.arrange(b,c, nrow =2, ncol = 1)

```

<br>

##### 1.3.3 Land
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# General graphing
ggplot(data_factor, aes(x = land_acres , y = sold_price)) + 
    geom_point(aes(color = infections_period), alpha = 0.15) + 
    geom_smooth(aes(color = infections_period)) +
    geom_smooth(color = "grey50", linetype = "dashed") +
    theme_minimal()

ggplot(data_factor, aes(x = land_acres, y = sold_price/land_acres)) + 
    geom_point(aes(color = infections_period), alpha = 0.15) + 
    geom_smooth(aes(color = infections_period)) +
    geom_smooth(color = "grey50", linetype = "dashed") +
    theme_minimal()
```

<br>

##### 1.3.4 Non-linear Additions

```{r, echo=TRUE}
#Additions
data_factor_core_clean <- data_factor_core
data_factor_core_clean$age_2 <- I(data_factor_core$age^2)
data_factor_core_clean$area_living_2 <- I(data_factor_core$area_living^2)
```

<br>

#### 1.4 Accounting for Multicollinearity
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# Full model summary
summ(lm_pre_alpha)

# Check Variance Inflation Factors (VIF)
VIF(lm_pre_alpha)
alias(lm_pre_alpha)

# Total area and living area are found to be significantly (i.e. VIF > 5) multicolinear (expected)
# Solution: Remove area_total

# Note the significant drop in R^2 from 0.99 to 0.86
lm_pre_alpha_cleaned <- lm(log(sold_price) ~ . - area_total ,data = data_factor_core)
summ(lm_pre_alpha_cleaned)
VIF(lm_pre_alpha_cleaned)

# Final pre_alpha
VIF(lm_pre_alpha_cleaned)
alias(lm_pre_alpha_cleaned)

# Another way to check for multicollinearity is visually through the mcvis package
data_numeric <- select_if(data_factor_core, is.numeric) # Subset numeric columns with dplyr
mcvis_result <- mcvis(X = data_numeric)
a <- plot(mcvis_result)

par(mfrow = c(2,2))
#Removals
data_numeric <- subset(data_numeric, select = -c(list_price))
mcvis_result <- mcvis(X = data_numeric)
b <- plot(mcvis_result)

#Removals
data_numeric <- subset(data_numeric, select = -c(area_total))
mcvis_result <- mcvis(X = data_numeric)
c <- plot(mcvis_result)

a
b
c



```

```{r, echo=FALSE,out.width="49%", out.height="20%",fig.cap="caption",fig.show='hold',fig.align='center'}

install.packages("cowplot")
install.packages("magick")
library(magick)
library(cowplot)
library(ggplot2)

p1 <- ggdraw() + draw_image("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Writing & Literature/Graphics from pptx/Multi_colin/multi_co1.png", scale = 1)
p2 <- ggdraw() + draw_image("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Writing & Literature/Graphics from pptx/Multi_colin/Multi_co2.png", scale = 1)

p3 <- ggdraw() + draw_image("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Writing & Literature/Graphics from pptx/Multi_colin/Multi_co3.png", scale = 1)

plot_grid(p1, p2, p3)
``` 

<br>

##### 1.4.1 Multicollinearity Removals
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# Removals
# - Area_total
# - Listing price

data_factor_core_clean <- subset(data_factor_core_clean, select = -c(area_total, list_price))
```

<br>

#### 1.5 High-leverage Removals
```{r}

data_factor_core_clean <- data_factor_core_clean[-c(23515), ]

```


<br>

### 1.5 Alpha Model
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}

# Finalized base model
lm_alpha <- glm(sold_price ~ . ,data = data_factor_core_clean)


summ(lm_alpha)
coeftest(lm_alpha, vcov = vcovHC(lm_alpha, method = "White2", type = "HC0"))

par(mfrow = c(2,2))
plot(lm_alpha)




install.packages("gtsummary")
library(gtsummary)

select()

gtsummary::tbl_regression(lm_alpha, exponentiate = TRUE)

```

<br>

#### 2. Factor Analysis

##### 2.1 Corona
###### 2.1.1 Visualization
```{r, attr.output='style="max-height: 250px;"'}

# Waves of infection
ggplot(data_factor, aes(x = as.Date(sold_date), y = infections_3mma)) + 
    geom_point(color = "grey35") + 
    geom_smooth(linetype = "dashed", color = "gray46") +
    theme_minimal() +
    scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    scale_y_continuous(limits = c(0,max(infections_3mma))) +
    xlab(" ") +
    ylab("Confirmed Infections per Day") +
    labs(title = "Waves of Infection",
         caption = "") +
    geom_vline(xintercept = as.numeric(as.Date("2020-03-23")), linetype=4)

# Accumulation of infections
ggplot(data_factor, aes(x = as.Date(sold_date), y = I(infections_accum/1000))) + 
    geom_point(color = "grey35") + 
    geom_smooth(linetype = "dashed", color = "gray46") +
    theme_minimal() +
    scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    scale_y_continuous(limits = c(0,max(I(infections_accum/1000)))) +
    xlab(" ") +
    ylab("Accumulation of Infections (in 000's") +
    labs(title = "Accumulation of Infections",
         caption = "")

# Infections and home prices
ggplot(data_factor, aes(x = I(infections_3mma/1000), y = sold_price)) + 
    #geom_point() + 
    geom_smooth(linetype = "dashed", color = "gray46") +
    theme_minimal() +
    scale_x_continuous( limits = c(0,max(I(infections_3mma/1000)))) +
    xlab("3-Month Moving Average of Daily Infections (in 000's)") +
    ylab("Sold Price (Actual)") +
    labs(title = "Infections and Price",
         caption = "")

#Price on Infections
very_low <- "#460f5c"
low <- "#2c728e"
med <- "#27ad81"
high <- "#f4e61e"

# "#ff6c67", "#00c2c6"

ggplot(data_factor, aes(x = infections_period, y = sold_price, fill = infections_period)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)) +
    ggtitle("Comparison of Sold Price") +
    xlab("Infections Present (1 = yes)") +
    scale_fill_manual(values=c(ver, med))
```

<br><br>

###### 2.1.2 Modeling
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# Testing Corona
lm_corona <- lm(sold_price ~ infections_3mma + . 
                
                ,data = data_factor_core_clean)

summ(lm_corona)
coeftest(lm_corona, vcov = vcovHC(lm_corona, method = "White2", type = "HC0"))

# Visualizing marginal effect per positive tests on price
lm_corona_single <- lm(sold_price ~ infections_3mma 
                
                ,data = data_factor_core_clean)
summ(lm_corona_single)    

ggpredict_1 <- ggpredict(lm_corona, terms = "infections_3mma")
ggpredict_2 <- ggpredict(lm_corona_single, terms = "infections_3mma")

# Plots
ggplot(data_factor_core, aes(x = infections_3mma)) +
   geom_smooth(data_factor_core, mapping = aes(y = sold_price), color = "grey50") + # Actual Data
   geom_smooth(ggpredict_1, mapping = aes(x, predicted), linetype = "dashed", color = "darkred") + # Controlled model
   geom_smooth(ggpredict_2, mapping = aes(x, predicted), linetype = "dashed", color = "darkblue") + # Best single fit
   ggtitle("Model Fit Overview") 
 
# Predicting infections with house prices
lm_flip <- lm_flip <- lm(infections_3mma ~ sold_price , data = data_factor)
summ(lm_flip)

ggpredict_flip <- ggpredict(lm_flip, terms = "sold_price")

ggplot(data_factor, aes(x = sold_price)) +
   geom_smooth(data_factor, mapping = aes(y = infections_3mma), color = "grey50") +
   geom_smooth(ggpredict_flip, mapping = aes(x, predicted), linetype = "dashed", color = "darkred") +
   labs(title = "Flipped Regression", subtitle = "Explining Infections using Variations in Price",
         caption = "") 

```

<br>

##### 2.2 Corona on Number of Bedrooms

###### 2.2.1 Visualiztion
```{r, warning=FALSE}

# Distribution
# Find the mean of each group
library(plyr)
data_factor$beds_total <- as.numeric(data_factor$beds_total)
room_mean <- ddply(data_factor, "infections_period", summarise, beds_mean=mean(beds_total, na.rm = TRUE))

data_factor$beds_total <- as.numeric(data_factor$beds_total)
a <- ggplot(data_factor, aes(x=beds_total, fill = infections_period)) +
    geom_density(alpha = 0.5, position = "identity") +
    scale_fill_manual(values=c(very_low, med)) +
    labs(title = "Distibution of Number of Bedrooms") +
    geom_vline(data=room_mean, aes(xintercept = room_mean[2,2]), linetype="dashed", size= 0.4, color = very_low, alpha = 0.5) +
    geom_vline(data=room_mean, aes(xintercept = room_mean[1,2]), linetype="dashed", size= 0.4, alpha = 0.5) +
    xlab("Number of Bedrooms") +
    ylab("Density") +
    labs(fill = "Infection Period")


# Distribution of total price and number of beds
data_factor$beds_total <- as.factor(data_factor$beds_total)
b <- ggplot(data = subset(data_factor, !is.na(beds_total)), aes(x = beds_total, y = sold_price, fill = beds_total)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    #coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)) +
      labs(title = "Distributions of Sold Price by Number of Bedrooms",
         caption = "") +
      xlab("Number of Bedrooms") +
      ylab("Sold Price")

      #+
      #scale_fill_manual(values = c(very_low, med), 
      #                name = "Infection Period",
      #                labels = c("Pre", "Post"))

# Distribution of price and number of beds before and after corona period
c <- ggplot(data = subset(data_factor, !is.na(beds_total)), aes(x = beds_total, y = sold_price, fill = beds_total)) +
    geom_violin(data = subset(data_factor, !is.na(beds_total)), mapping = aes(alpha = 0.5, fill = infections_period)) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    #coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)) +
      labs(title = "Distributions of Sold Price by Number of Bedrooms", 
           subtitle = "Price Pre vs. Post Infection Period",
           caption = "") +
      xlab("Number of Bedrooms")  +
      ylab("Sold Price")

# Distribution of price per sqft. and number of beds
data_factor$beds_total <- as.factor(data_factor$beds_total)
d <- ggplot(data = subset(data_factor, !is.na(beds_total)), aes(x = beds_total, y = sold_price/area_living, fill = beds_total)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    #coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)) +
      labs( title = "Distributions of Sold Price by Number of Bedrooms", subtitle = "Sold Price Per Sqft.",
         caption = "") +
      xlab("Number of Bedrooms") +
      ylab("Sold Price per Sqft.")
  

# Distribution of price per sqft. and number of beds before and after corona period
e <- ggplot(data = subset(data_factor, !is.na(beds_total)), aes(x = beds_total, y = sold_price/area_living , fill = beds_total)) +
    geom_violin(data = subset(data_factor, !is.na(beds_total)), mapping = aes(alpha = 0.5, fill = infections_period)) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    #coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)) +
      labs( title = "Distributions of Sold Price by Number of Bedrooms", subtitle = "Sold Price Per Sqft. Pre vs. Post Infection Period",
         caption = "") +
      xlab("Number of Bedrooms")  +
      ylab("Sold Price per Sqft.")

gridExtra::grid.arrange(a)
gridExtra::grid.arrange(b)
gridExtra::grid.arrange(c)
gridExtra::grid.arrange(d)
gridExtra::grid.arrange(e)
#gridExtra::grid.arrange(b,c, ncol = 2)


```

###### 2.2.2 Modeling

Ideas

* Break into each room number

```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# Note on bedroom's relationship with all other size-related features:
#  - The interpretation of the coefficient is dependent on the other fixed size features, especially area_living. In the case that total area is fixed, the interpretation of this coefficient become the effect of more bedrooms for a fixed size. No one wants a 500 sqft. house with 8 bedrooms.  
#  - For this reason, when analyzing changes in bedrooms, total size is excluded

# Change data structure to factor
data_factor_core_clean$beds_total <- as.factor(data_factor_core_clean$beds_total)

# Single Model: Factor
lm_corona_bedrooms_single <- lm(sold_price ~ + beds_total ,data = data_factor_core_clean)
summ(lm_corona_bedrooms_single)
coeftest(lm_corona_bedrooms_single, vcov = vcovHC(lm_corona_bedrooms_single, method = "White2", type = "HC0"))

# Basic Test: Few Controls
lm_corona_bedrooms_basic <- lm(sold_price ~ 
                      + data_factor$infections_3mma + beds_total + data_factor$infections_3mma*beds_total 

                       # Removals
                       - area_living
                       - area_living_2 
                       - bath_full
                       - bath_half
                       - land_acres
                       - sold_date
                       - garage
                       - property_type
                      
                            ,data = data_factor_core_clean)
summ(lm_corona_bedrooms_basic)
coeftest(lm_corona_bedrooms_basic, vcov = vcovHC(lm_corona_bedrooms_basic, method = "White2", type = "HC0"))

# General Model: Controlled
lm_corona_bedrooms <- lm(sold_price ~ . +
               
                       # test variable(s)                    
                       + data_factor$infections_3mma + beds_total + data_factor$infections_3mma*beds_total
                       
                       # Removals
                       - area_living
                       - area_living_2 
                       - bath_full
                       - bath_half
                       - land_acres
                       - sold_date
                       - garage
                       - property_type
                       
                       ,data = data_factor_core_clean)
summ(lm_corona_bedrooms)
coeftest(lm_corona_bedrooms, vcov = vcovHC(lm_corona_bedrooms, method = "White2", type = "HC0"))

```

<br>

##### 2.3 Corona on Price Quantiles

###### 2.3.1 Visualization
```{r}

# Find the mean of each group
library(plyr)
price_means <- ddply(data_factor, "infections_period", summarise, price_mean = mean(sold_price, na.rm = TRUE))

# Distribution: Total
ggplot(data_factor, aes(x = sold_price)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    ggtitle("Price Distribution") +
    geom_vline(data=price_means, aes(xintercept = mean(sold_price)), linetype="dashed", size= 0.4, color = very_low, alpha = 0.8) +
    xlab("Sold Price") +
    ylab("Density") 

# Distribution: Infection
ggplot(data_factor, aes(x = sold_price, fill = infections_period)) +
    geom_density(alpha = 0.5, position = "identity") +
    ggtitle("Price Distributions") +
    geom_vline(data=price_means, aes(xintercept = price_means[2,2]), linetype="dashed", size= 0.4, color = med, alpha = 0.8) +
    geom_vline(data = price_means, aes(xintercept = price_means[1,2]), linetype="dashed", size= 0.4, color = very_low, alpha = 0.8) +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post")) +
    xlab("Sold Price") +
    ylab("Density") +
    labs(fill = "Infection Period")

# Distribution: Top vs. Bottom
ggplot(data_factor) +
    geom_density(aes(x = sold_price, fill = infections_period), alpha = 0.5, position = "identity") + 
    facet_grid(vars(top25_sold_price, bottom25_sold_price), scales = "free") +
    ggtitle("Price Distributions") +
    scale_fill_manual(values=c(very_low, med)) +
    xlab("Sold Price") +
    labs(fill = "Infection Period") +
    ylab("Density") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))

#Price and Infections
ggplot(data_factor, aes(x = infections_period, y = sold_price, fill = infections_period)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)) +
    ggtitle("Comparison of Sold Price") +
    xlab("Infection Period") +
    scale_fill_manual(values=c(very_low, med)) +
    ylab("Sold Price") 

```

<br>

###### 2.3.2 Modeling
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}

# Testing Corona, top 25% in price ---------------------------------------------------------------------

# Single Var Test
lm_corona_price_top_single <- lm(sold_price ~ . 
               
                       # test variable(s)                    
                       + data_factor$top50_sold_price
                       
                       # Removals
                       
                       ,data = data_factor_core_clean)
summ(lm_corona_price_top_single)
coeftest(lm_corona_price_top_single, vcov = vcovHC(lm_corona_price_top_single, method = "White2", type = "HC0"))


# General Model: No Controls 
lm_corona_price_top_basic <- lm(sold_price ~ +
               
                       # test variable(s)                    
                       + data_factor$infections_3mma + data_factor$top50_sold_price 
                       + data_factor$infections_3mma*data_factor$top50_sold_price
                       
                       # Removals
                       
                        ,data = data_factor_core_clean)
summ(lm_corona_price_top_basic)
coeftest(lm_corona_price_top_basic, vcov = vcovHC(lm_corona_price_top_basic, method = "White2", type = "HC0"))

# General Model: With Controls 
lm_corona_price_top <- lm(sold_price ~ . +
               
                       # test variable(s)                    
                       + data_factor$infections_3mma + data_factor$top50_sold_price 
                       + data_factor$infections_3mma*data_factor$top50_sold_price
                       
                       # Removals
                       
                       ,data = data_factor_core_clean)
summ(lm_corona_price_top)
coeftest(lm_corona_price_top, vcov = vcovHC(lm_corona_price_top, method = "White2", type = "HC0"))

# Testing Corona, Bottom 25% in price ------------------------------------------------------------------

# Single Var Test
lm_corona_price_bottom_single <- lm(sold_price ~ +
               
                       # test variable(s)                    
                       + bottom25_sold_price
                       
                       # Removals
                       
                       ,data = data_factor_core_clean)
summ(lm_corona_price_bottom)
coeftest(lm_corona_price_bottom_single, vcov = vcovHC(lm_corona_price_bottom_single, method = "White2", type = "HC0"))

# General Model: No controls
lm_corona_price_bottom_basic <- lm(sold_price ~ +
               
                       # test variable(s)                    
                       + data_factor$infections_3mma + bottom25_sold_price +
                         data_factor$infections_3mma*bottom25_sold_price
                       
                       # Removals
                       
                       ,data = data_factor_core_clean)
summ(lm_corona_price_bottom_basic)
coeftest(lm_corona_price_bottom_basic, vcov = vcovHC(lm_corona_price_bottom_basic, method = "White2", type = "HC0"))

# General Model: With controls
lm_corona_price_bottom <- lm(sold_price ~ . +
                         
                       # test variable(s)                    
                       + data_factor$infections_3mma + bottom25_sold_price
                       + data_factor$infections_3mma*bottom25_sold_price
                         
                       # Removals
                       - sold_date
                         
                         ,data = data_factor_core_clean)
summ(lm_corona_price_bottom)
coeftest(lm_corona_price_bottom, vcov = vcovHC(lm_corona_price_bottom, method = "White2", type = "HC0"))

```

<br>

##### 2.4 Corona on Age Quantiles

###### 2.4.1 Visualization
```{r}
# Conditional Mean
library(plyr)
age_mean_data <- ddply(data_factor, "infections_period", summarise, age_mean = mean(age, na.rm = TRUE))

# Distribution: Total
ggplot(data_factor, aes(x = age)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    ggtitle("Age Distribution") +
    geom_vline(aes(xintercept = mean(age)), linetype="dashed", size= 0.4, alpha = 0.5, color = very_low) +
    xlab("Age of Property") +
    ylab("Density")


# Distribution: Infection
ggplot(data_factor, aes(x = age, fill = infections_period)) +
    geom_density(alpha = 0.5, position = "identity") +
    ggtitle("Age Distributions") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post")) +
    geom_vline(data = age_mean_data, aes(xintercept = age_mean_data[2,2]), linetype="dashed", size= 0.5, color = med, alpha = 0.8) +
    geom_vline(data = age_mean_data, aes(xintercept = age_mean_data[1,2]), linetype="dashed", size= 0.5, alpha = 0.8, color = very_low) +
    xlab("Age of Property") +
    ylab("Density")

?scale_fill_discrete()

# Distribution: Top vs. Bottom
ggplot(data_factor) +
    geom_density(aes(x = age, fill = infections_period), alpha = 0.5, position = "identity") + 
                     facet_grid(vars(top25_age, bottom25_age), scales = "free") +
                     ggtitle("Age Distributions") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post")) +
    labs(fill = "Infection Period") +
    xlab("Age of Property") +
    ylab("Density")

#Age on Infections
ggplot(data_factor, aes(x = infections_period, y = age, fill = infections_period)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)) +
    ggtitle("Comparison of Age") +
    xlab("Infection Period") +
    ylab("Age of Property") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))




```

###### 2.4.2 Modeling
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# Testing Corona, top 25% in age
lm_corona_age_top_single <- lm(sold_price ~
               
                        # test variable(s)                    
                        + top25_age
                       
                        # Removals
                        - age
                        - age_2
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_age_top_single, vcov = vcovHC(lm_corona_age_top_single, method = "White2", type = "HC0"))

lm_corona_age_top <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + data_factor$infections_3mma + top25_age + data_factor$infections_3mma*top25_age 
                       
                        # Removals
                        - age
                        - age_2
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_age_top, vcov = vcovHC(lm_corona_age_top, method = "White2", type = "HC0"))

# Testing Corona, bottom 25% in age
lm_corona_age_bottom_single <- lm(sold_price ~ 
               
                        # test variable(s)                    
                        + bottom25_age
                       
                        # Removals
                        - age
                        - age_2
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_age_bottom, vcov = vcovHC(lm_corona_age_bottom, method = "White2", type = "HC0"))

lm_corona_age_bottom <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + data_factor$infections_3mma + bottom25_age + data_factor$infections_3mma*bottom25_age 
                       
                        # Removals
                        - age
                        - age_2
                        - property_type
                        - area_living
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_age_bottom, vcov = vcovHC(lm_corona_age_bottom, method = "White2", type = "HC0"))

```

<br>

##### 2.5 Corona on Size Quantiles

###### 2.5.1 Visualization
```{r}
# Conditional Mean
library(plyr)
area_living_mean_data <- ddply(data_factor, "infections_period", summarise, area_living_mean = mean(area_living, na.rm = TRUE))

# Distribution: Total
ggplot(data_factor, aes(x = area_living)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    ggtitle("Living Area Distribution") +
    geom_vline(aes(xintercept = mean(area_living)), linetype="dashed", size= 0.4, alpha = 0.5, color = very_low) +
    xlab("Living Area") +
    ylab("Density")


# Distribution: Infection
ggplot(data_factor, aes(x = area_living, fill = infections_period)) +
    geom_density(alpha = 0.5, position = "identity") +
    ggtitle("Living Area Distributions") +
    geom_vline(data = area_living_mean_data, aes(xintercept = area_living_mean_data[2,2]), linetype="dashed", size= 0.5, color = med, alpha = 0.8) +
    geom_vline(data = area_living_mean_data, aes(xintercept = area_living_mean_data[1,2]), linetype="dashed", size= 0.5, alpha = 0.8, color = very_low) +
    xlab("Living Area") +
    ylab("Density") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))

# Distribution: Top vs. Bottom
ggplot(data_factor) +
    geom_density(aes(x = area_living, fill = infections_period), alpha = 0.5, position = "identity") + 
    facet_grid(vars(top25_area_living, bottom25_area_living), scales = "free") +
    ggtitle("Living Area Distributions") +
    xlab("Living Area") +
    ylab("Density") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))

#area_living on Infections
ggplot(data_factor, aes(x = infections_period, y = area_living, fill = infections_period)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)) +
    ggtitle("Comparison of Living Area") +
    xlab("Infection Period") +
    ylab("Living Area") +
    scale_fill_manual(values=c(very_low, med))




```

###### 2.5.2 Modeling
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# Testing Corona, top 25% in area_living
lm_corona_area_living_top_single <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + top25_area_living
                       
                        # Removals
                        - area_living
                        - area_living_2
                        - beds_total
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_area_living_top_single, vcov = vcovHC(lm_corona_area_living_top_single, method = "White2", type = "HC0"))

lm_corona_area_living_top <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + data_factor$infections_3mma + top25_area_living + data_factor$infections_3mma*top25_area_living 
                       
                        # Removals
                        - area_living
                        - area_living_2
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_area_living_top, vcov = vcovHC(lm_corona_area_living_top, method = "White2", type = "HC0"))

# Testing Corona, bottom 25% in area_living
lm_corona_area_living_bottom_single <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + bottom25_area_living
                       
                        # Removals
                        - area_living
                        - area_living_2
                        - beds_total
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_area_living_bottom_single, vcov = vcovHC(lm_corona_area_living_bottom_single, method = "White2", type = "HC0"))

lm_corona_area_living_bottom <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + data_factor$infections_3mma + bottom25_area_living + data_factor$infections_3mma*bottom25_area_living 
                       
                        # Removals
                        - area_living
                        - area_living_2
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_area_living_bottom, vcov = vcovHC(lm_corona_area_living_bottom, method = "White2", type = "HC0"))
```

<br>

##### 2.6 Corona on Days on Market

###### 2.6.1 Visualization
```{r}
# Conditional Mean
library(plyr)
dom_mean_data <- ddply(data_factor, "infections_period", summarise, dom_mean = mean(dom, na.rm = TRUE))

# Distribution: Just for City
ggplot(data_factor, aes(x = dom)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    ggtitle("Days on Market Distribution") +
    geom_vline(aes(xintercept = mean(dom)), linetype="dashed", size= 0.4, alpha = 0.5, color = very_low) +
    xlab("Days on Market") +
    ylab("Density")


# Distribution: Infection
ggplot(data_factor, aes(x = dom, fill = infections_period)) +
    geom_density(alpha = 0.5, position = "identity") +
    ggtitle("Days on Market Distributions") +
    geom_vline(data = dom_mean_data, aes(xintercept = dom_mean_data[2,2]), linetype="dashed", size= 0.5, color = med, alpha = 0.8) +
    geom_vline(data = dom_mean_data, aes(xintercept = dom_mean_data[1,2]), linetype="dashed", size= 0.5, alpha = 0.8, color = very_low) +
    xlab("Days on Market") +
    ylab("Density") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))

# Distribution: Top vs. Bottom
ggplot(data_factor) +
    geom_density(aes(x = dom, fill = infections_period), alpha = 0.5, position = "identity") + 
    facet_grid(vars(top25_dom, bottom25_dom), scales = "free") +
    ggtitle("Days on Market Distributions") +
    xlab("Days on Market") +
    ylab("Density") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))

#dom on Infections
ggplot(data_factor, aes(x = infections_period, y = dom, fill = infections_period)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    #coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)) +
    ggtitle("Comparison of Days on Market") +
    xlab("Infection Period") +
    ylab("Days on Market") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))

```
###### 2.6.2 Modeling
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# Testing Corona, top 25% in dom
lm_corona_dom_top_single <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + top25_dom
                       
                        # Removals
                        - dom
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_dom_top_single, vcov = vcovHC(lm_corona_dom_top_single, method = "White2", type = "HC0"))

lm_corona_dom_top <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + data_factor$infections_3mma + top25_dom + data_factor$infections_3mma*top25_dom 
                       
                        # Removals
                        - dom
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_dom_top, vcov = vcovHC(lm_corona_dom_top, method = "White2", type = "HC0"))

# Testing Corona, bottom 25% in dom
lm_corona_dom_bottom_single <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + bottom25_dom
                       
                        # Removals
                        - dom
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_dom_bottom_single, vcov = vcovHC(lm_corona_dom_bottom_single, method = "White2", type = "HC0"))

lm_corona_dom_bottom <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + data_factor$infections_3mma + bottom25_dom + data_factor$infections_3mma*bottom25_dom 
                       
                        # Removals
                        - dom
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_dom_bottom, vcov = vcovHC(lm_corona_dom_bottom, method = "White2", type = "HC0"))

# top 25% is too tight!! means aren't different

# this means that the premium for being in the bottom percentile of dom decreased. This make's sense because this was no longer a result of increased quality but increased demand.
```

<br>

##### 2.7 Corona on City

###### 2.7.1 Visualization
```{r, alpha = false}
# Conditional Mean
library(plyr)
city_limits_mean_data <- ddply(subset(data_factor, data_factor$city_limits ==1), "infections_period", summarise, city_limits_mean = mean(sold_price, na.rm = TRUE))

# Distribution: Just City
ggplot(data = subset(data_factor, data_factor$city_limits ==1), aes(x = sold_price)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    ggtitle("Price Distribution of Properties in City Limits") +
    geom_vline(aes(xintercept = mean(city_limits)), linetype="dashed", size= 0.4, alpha = 0.5) +
    xlab("Sold Price") +
    ylab("Density")

# Distribution: Infection
ggplot(data = subset(data_factor, data_factor$city_limits ==1), aes(x = sold_price, fill = infections_period)) +
    geom_density(alpha = 0.5, position = "identity") +
    ggtitle("Price Distributions of Properties in City Limits") +
    geom_vline(data = city_limits_mean_data, aes(xintercept = city_limits_mean_data[2,2]), linetype="dashed", size= 0.5, color = med, alpha = 0.8) +
    geom_vline(data = city_limits_mean_data, aes(xintercept = city_limits_mean_data[1,2]), linetype="dashed", size= 0.5, alpha = 0.8, color = very_low) +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post")) +
    xlab("Sold Price") +
    ylab("Density") 

#city_limits on Infections
ggplot(data_factor, aes(x = city_limits, y = sold_price, fill = infections_period)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1, alpha = 0.9) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    #coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)) +
    ggtitle("Comparison of Price: City Limts and Pre vs. Post Corona") +
    xlab("City Limits and Infection Period") +
    ylab("Sold Price") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "City Limits",
                      labels = c("Pre", "Post"))

```

###### 2.7.2 Modeling
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}

# Testing Corona, City Limits
lm_corona_city <- lm(sold_price ~ . 
               
                       # test variable(s)                    
                       + data_factor$infections_3mma + data_factor$city_limits 
                       + data_factor$infections_3mma*data_factor$city_limits
                       
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_city, vcov = vcovHC(lm_corona_city, method = "White2", type = "HC0"))
```

<br><br>

#### 3. Playground

<br>

##### 3.1 Index creation

```{r}

data_index <- read_excel("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Models/Data/New Data/Index_hardkey.xlsx")
attach(data_index)

data_index_fred <- read_excel("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Models/Data/New Data/Index_FRED.xls")
attach(data_index_fred)

data_index_gdp <- read_excel("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Models/Data/New Data/la_GDP.xls")
attach(data_index_gdp)

data_index_fred_1975_total <- read_excel("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Models/Data/New Data/Total_US_1975.xls")
attach(data_index_fred_1975_total)

# Index graphing
ggplot(data_index, aes(x = Date)) +
    geom_line(mapping = aes(y = lma_2m_index), color = "darkred") +
    geom_line(mapping = aes(y = lma_3m_index), color = "darkgreen") +
    geom_line(mapping = aes(y = lma_4m_index), color = "darkblue") +
    geom_line(mapping = aes(y = lma_5m_index), color = "grey45") +
    geom_vline(xintercept = as.numeric(as.Date("2020-03-23")), linetype=4, color = "green") +
    #scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    scale_y_continuous(limits = c(min(lma_2m_index),max(lma_2m_index))) +
    xlab(" ") +
    ylab("Weighted Average Price per Sqft.") +
    labs(title = "Louisiana Housing Index",
         caption = "") 

# FRED quarterly data
ggplot(data_index_fred, aes(x = date)) + 
    geom_line(aes(y = index_Q1_1980), color = "darkred") +
    theme_minimal() +
    geom_vline(xintercept = as.Date("2020-01-01"), linetype=4, color = "green") +
    #scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    scale_y_continuous(limits = c(min(index_Q1_1980),max(index_Q1_1980))) +
    xlab(" ") +
    ylab("Index Value") +
    labs(title = "Louisiana Housing Index: FRED St. Louis",
         caption = "") 

# La Real GDP data quarterly data
data_index_gdp <- subset(data_index_gdp, data_index_gdp$date >= as.Date("2011-07-01"))
ggplot(data_index_gdp, aes(x = date)) + 
    geom_line(aes(y = real_gdp_Index, color = "darkred"), linetype = "dashed", size = .5) +
    geom_line(aes(y = real_gdp_re_specific_index, color = "darkblue"), size = .5) +
    theme(legend.position = "bottom") +
    geom_vline(xintercept = as.Date("2020-01-01"), linetype = 4, color = "green") +
    #scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    #scale_y_continuous(limits = c(min(real_gdp_Index),max(real_gdp_Index))) +
    xlab(" ") +
    ylab("Index Value") +
    labs(title = "Louisiana GDP and Housing Index: FRED St. Louis",
         caption = "") +
         scale_color_discrete(name = "Infection Period",
                              labels = c("RE Index", "Aggrigate GDP Index"))

cor.test(real_gdp_Index, real_gdp_re_specific_index)

# TOTAL US Real GDP data quarterly data base 2011
ggplot(data_index_fred_total, aes(x = observation_date)) + 
    geom_line(aes(y = GDP, color = very_low), linetype = "dashed", size = .5) +
    geom_line(aes(y = all_re_index, color = med), size = .5) +
    theme(legend.position = "bottom" ) +
    geom_vline(xintercept = as.Date("2020-01-01"), linetype=4, color = "green") +
    #scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    #scale_y_continuous(limits = c(min(real_gdp_Index),max(real_gdp_Index))) +
    xlab(" ") +
    ylab("Index Value") +
    labs(title = "Total US GDP and Housing Index: FRED St. Louis",
         caption = "") +
         scale_color_discrete(name = "Infection Period",
                              labels = c("RE Index", "Aggrigate GDP Index"))
  
                    
# TOTAL US Real GDP data quarterly data base 1975

# Nominal
ggplot(data_index_fred_1975_total, aes(x = date)) + 
    geom_line(aes(y = gdp_pc_nom_index_1975 , color = very_low), linetype = "dashed", size = .5) +
    geom_line(aes(y = re_nom_index_1975, color = med), size = .5) +
    theme(legend.position = "bottom" ) +
    #geom_vline(xintercept = as.Date("2020-01-01"), linetype=4, color = "green") +
    #scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    #scale_y_continuous(limits = c(min(real_gdp_Index),max(real_gdp_Index))) +
    xlab(" ") +
    ylab("Index Value (1975 Q1 = 100)") +
    labs(title = "U.S. GDP and Housing Index",
         caption = "FRED, St. Louis") +
         scale_color_discrete(name = "",
                              labels = c("Nominal Housing Prices", "Nominal GDP Per-Capita"))

corr_nom_1975 <- cor(gdp_pc_nom_index_1975, re_nom_index_1975)


# Real
ggplot(data_index_fred_1975_total, aes(x = date)) + 
    geom_line(aes(y = gdp_pc_real_index_1975 , color = very_low), linetype = "dashed", size = .5) +
    geom_line(aes(y = re_real_index_1975, color = med), size = .5) +
    theme(legend.position = "bottom" ) +
    #geom_vline(xintercept = as.Date("2020-01-01"), linetype=4, color = "green") +
    #scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    #scale_y_continuous(limits = c(min(real_gdp_Index),max(real_gdp_Index))) +
    xlab(" ") +
    ylab("Index Value (1975 Q1 = 100)") +
    labs(title = "U.S. GDP and Housing Index",
         caption = "FRED, St. Louis") +
         scale_color_discrete(name = "",
                              labels = c("Real Housing Prices", "Real GDP Per-Capita"))

corr_real_1975 <- cor(gdp_pc_real_index_1975, re_real_index_1975)

corr_nom_1975
corr_real_1975


```

##### 3.2 Playing with Maps
```{r}
# packages
require(ggplot2)
install.packages("ggmap")
require(maps)
install.packages(Geoc)



#Basic Map
LA <- map_data("state", region="louisiana")
ggplot(LA, aes(x=long, y=lat))+geom_polygon()


# data
salesCalls <- data.frame(State=rep("louisiana",5), 
                             City=c("Baton Rouge","New Orleans", "Shreveport",       "Lafayette", "Mandeville"),
                             Calls=c(10,5,8,13,2))

salesCalls <- cbind(geocode(as.character(salesCalls$City)), salesCalls)



?cbind

ggplot(LA, aes(x=long, y=lat)) +
  geom_polygon() +
  coord_map() +
  geom_point(data=salesCalls, aes(x=lon, y=lat, size=Calls), color="orange")


```

##### 3.3 Reduction in Dimensionality

```{r}
library(boot) # K-fold
library(leaps) # Subset 
library(glmnet) #glmnet() is the main function in the glmnet package (must pass in an x matrix as well as a y vector)

# Set x-y definitions for glmnet package 
x <- model.matrix(sold_price ~ . ,data = data_factor_core_clean)[, -1]

y <- data_factor_core_clean$sold_price[1:24653] # Manually restricted due rows not matching with x 'x' for an unknown reason

# General grid
grid <- exp(seq(10, -65, length = 101)) #grid of values from exp(10) [null model] to exp(-15) [least squares]

#Lasso
set.seed(1)
cv.out <- cv.glmnet(x, y, alpha = 1, lambda = grid, nfolds = 10) #lasso
plot(cv.out)

# Base decision
bestlam <- cv.out$lambda.min; bestlam; log(bestlam)
out <- cv.out$glmnet.fit
lasso.coef <- predict(out, type = "coefficients", s = bestlam); lasso.coef; lasso.coef[lasso.coef != 0]
sum(abs(lasso.coef[1:31])) #l1 norm

# +1se decision
bestlam2 <- cv.out$lambda.1se; bestlam2; log(bestlam2)
lasso.coef2 <- predict(out, type = "coefficients", s = bestlam2); lasso.coef2; lasso.coef2[lasso.coef2 != 0]
sum(abs(lasso.coef2[2:31])) #l1 norm

```


##### 3.4 Basic 3D Graphing
```{r}
kd <- with(MASS::geyser, MASS::kde2d(sold_price, infections_3mma, n = 50))

fig <- plot_ly(x = kd$x, y = kd$y, z = kd$z) %>% add_surface()

fig
```

##### 3.4 Descriptive Stats

```{r}
# Correlation Matrix heatmap
# Get numeric variable

data_factor$bath_full < as.numeric(data_factor$bath_full)
num_vars <- data_factor %>% dplyr::select(where(is.numeric))
num_vars <- subset(num_vars, select = -c(top50_sold_price))

# Corr matrix
cormat <- round(cor(num_vars),2)
head(cormat)

melted_cormat <- melt(cormat)
head(melted_cormat)

ggplot(data = melted_cormat, aes(x=Var1, y=Var2, fill = value)) + 
   geom_tile() +
   scale_fill_gradient2(low = very_low, 
                        high = high, 
                        mid = med, 
                        midpoint = 0, 
                        limit = c(-1,1), 
                        space = "Lab", 
                        name="Correlation") +
   theme_minimal() + 
   theme(axis.text.x = element_text(angle = 45, vjust = 1, size = 10, hjust = 1, color = "#2E2E2E"),
         axis.text.y = element_text(angle = 0, vjust = 1, size = 10, hjust = 1, color = "#2E2E2E")) +
   coord_fixed() +
   labs(title = "Correlation Matrix",
        x = "",
        y = "")


```

```{r}
# Distribution: Total
a <- ggplot(data_factor, aes(x = sold_price/1000)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Sold Price") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

b <- ggplot(data_factor, aes(x = list_price/1000)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("List Price") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 


c <- ggplot(data_factor, aes(x = area_living)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Living Area") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

d <- ggplot(data_factor, aes(x = land_acres)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Land in Acres") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

e <- ggplot(data_factor, aes(x = area_total)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Total Area") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

f <- ggplot(data_factor, aes(x = age)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Age") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

g <- ggplot(data_factor, aes(x = dom)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("DOM") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

data_factor$sold_date <- as.Date(data_factor$sold_date)
str(data_factor)
h <- ggplot(data_factor, aes(x = sold_date)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Sold Date") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) +
    scale_x_date(date_labels = "%Y")

i <- ggplot(data = subset(data_factor, data_factor$infections_daily > 1), aes(x = infections_daily)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Infections Daily") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

data_factor$beds_total <- as.numeric(data_factor$beds_total)
j <- ggplot(data_factor, aes(x=beds_total)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    scale_fill_manual(values=c(very_low)) +
    xlab("Number of Bedrooms") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

data_factor$bath_full <- as.numeric(data_factor$bath_full)
k <- ggplot(data_factor, aes(x=bath_full)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    scale_fill_manual(values=c(very_low)) +
    xlab("Number of Full Bathrooms") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

data_factor$bath_half <- as.numeric(data_factor$bath_half)
l <- ggplot(data_factor, aes(x=bath_half)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    scale_fill_manual(values=c(very_low)) +
    xlab("Number of Half Bathrooms") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

gridExtra::grid.arrange(a,b,c,d,e,f,g,h,i,j,k,l, nrow =4, ncol = 3)


```

##### 3.4 Simple UCLA Case 
```{r}
lm_ucla <- lm(sold_price ~ pool + infections_period + pool*infections_period, data = data_factor)
summ(lm_ucla)

# load package
library(sjPlot)
library(sjmisc)
library(sjlabelled)

tab_model(lm_ucla)

```




end of document
=======
---
title: "Hedonic Pricing Models"
output:
  html_notebook: default
  pdf_document: default
  word_document: default
code_folding: hide
Author: Sawyer Benson
---

### Sawyer Benson's Master Thesis 
### Janurary 10, 2022


```{r message=TRUE, warning=TRUE, include=FALSE, results='hide'}

#Read in packages and data

library(readxl) # Import excel data frames
library(ggplot2) # Graphs
library(scales) # Scale range of ggplots 
library(ggfortify) # Additional ggplot2 functionality
library(olsrr) # Testing for heteroscedasticity
library(lmtest) # Testing for heteroscedasticity using breuch-pagan
library(sandwich) # Amending heteroskedasticity 
library(mcvis) # Visualizing multicollinearity
library(gridExtra) # Organize graphs
library(dplyr) # data_factor wrangling
library(tidyr) # data_factor wrangling
library(tinytex) #for RMarkdown
library(openxlsx) #Export data frame into Excel
library(ggeffects) # plotting marginal effects
library(sjPlot) # plotting marginal effects
library(stargazer) # Showing several outputs next to each other in a STATA style
library(modelsummary) # Showing several outputs next to each other in a STATA style
library(regclass) # for testing multicollinearity using VIF
library(jtools) # cleaner regression output (e.g. summ(lm) 
library(tidyverse) # data cleaning
library(hrbrthemes) # special boxplots
library(viridis) # special boxplots
library(plotly) # For 3D plotting in ggplot2

# Import and attach data sets
data_factor <- read_excel("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Models/Data/New Data/3. data_factor_cleaned.xlsx")
attach(data_factor)

# Convert Char to Factors with N Levels
# Structure Change
data_factor$property_type <- as.factor(data_factor$property_type)
data_factor$ac_type <- as.factor(data_factor$ac_type)
data_factor$patio <- as.factor(data_factor$patio)
data_factor$school_general <- as.factor(data_factor$school_general)
data_factor$pool <- as.factor(data_factor$pool)
data_factor$roof_type <- as.factor(data_factor$roof_type)
data_factor$gas_type <- as.factor(data_factor$gas_type)
data_factor$out_building <- as.factor(data_factor$out_building)
data_factor$appliances <- as.factor(data_factor$appliances)
data_factor$garage <- as.factor(data_factor$garage)
data_factor$property_condition <- as.factor(data_factor$property_condition)
data_factor$energy_efficient <- as.factor(data_factor$energy_efficient)
data_factor$exterior_type <- as.factor(data_factor$exterior_type)
data_factor$exterior_features <- as.factor(data_factor$exterior_features)
data_factor$fireplace <- as.factor(data_factor$fireplace)
data_factor$foundation_type <- as.factor(data_factor$foundation_type)
data_factor$beds_total <- as.factor(data_factor$beds_total)
data_factor$bath_full <- as.factor(data_factor$bath_full)
data_factor$bath_half <- as.factor(data_factor$bath_half)
data_factor$sewer_type <- as.factor(data_factor$sewer_type)
data_factor$property_style <- as.factor(data_factor$property_style)
data_factor$subdivision <- as.factor(data_factor$subdivision)
data_factor$water_type <- as.factor(data_factor$water_type)
data_factor$waterfront <- as.factor(data_factor$waterfront)
data_factor$sold_date <- openxlsx::convertToDate(data_factor$sold_date)
data_factor$sold_date <- as.numeric(data_factor$sold_date)

str(data_factor)

# Splits
data_factor$city_limits <- as.factor(data_factor$city_limits)
data_factor$corona_date_split <- as.factor(data_factor$corona_date_split)
data_factor$top25_sold_price <- as.factor(data_factor$top25_sold_price)
data_factor$bottom25_sold_price <- as.factor(data_factor$bottom25_sold_price)
data_factor$top25_area_living <- as.factor(data_factor$top25_area_living)
data_factor$bottom25_area_living  <- as.factor(data_factor$bottom25_area_living)
data_factor$top25_age <- as.factor(data_factor$top25_age)
data_factor$bottom25_age <- as.factor(data_factor$bottom25_age)
data_factor$top25_dom <- as.factor(data_factor$top25_dom)
data_factor$bottom25_dom <- as.factor(data_factor$bottom25_dom)
data_factor$infections_period <- as.numeric(data_factor$infections_accum > 1000)
data_factor$infections_period <- as.factor(data_factor$infections_period)

str(data_factor)

# Remove this weird '20' level is bath_full
levels(data_factor$bath_full)
is.na(data_factor$bath_full) <- data_factor$bath_full == "20"
data_factor$bath_full <- factor(data_factor$bath_full)
levels(data_factor$bath_full)

# Remove beds_total > 5
levels(data_factor$beds_total)
is.na(data_factor$beds_total) <- data_factor$beds_total == "7" 
data_factor$beds_total <- factor(data_factor$beds_total)
is.na(data_factor$beds_total) <- data_factor$beds_total == "6" 
data_factor$beds_total <- factor(data_factor$beds_total)
levels(data_factor$beds_total)



levels(data_factor$beds_total)
levels(data_factor$bath_full)
levels(data_factor$bath_half)

# Data frame without Split Vars
names(data_factor)
data_factor_core <- data_factor[-c(36:47)]
data_factor_core <- subset(data_factor_core, select = -c(city_limits, mls_number, infections_period))
str(data_factor_core)
names(data_factor_core)


```


```{r include=FALSE}

# RMarkdown Code: Format chunk output into scroll lists
# Installed to limit the length of regression output
# save the built-in output hook
hook_output <- knitr::knit_hooks$get("output")

# set a new output hook to truncate text output
knitr::knit_hooks$set(output = function(x, options) {
  if (!is.null(n <- options$out.lines)) {
    x <- xfun::split_lines(x)
    if (length(x) > n) {
      # truncate the output
      x <- c(head(x, n), "....\n")
    }
    x <- paste(x, collapse = "\n")
  }
  hook_output(x, options)
})
``` 

### 1. Model Design: Checks & Corrections

#### 1.1 Accounting for Heteroskedasticity
```{r echo=TRUE, warning=FALSE, attr.output='style="max-height: 250px;"'}

# All-inclusive model
lm_pre_alpha <- lm(sold_price ~ . , data = data_factor_core)
summ(lm_pre_alpha)

# pre_alphaing for heteroskedasticity
#  a. Graphically
par(mfrow = c(2,2))
plot(lm_pre_alpha)

#autoplot(lm_pre_alpha)

#  b. Statistically
ols_test_breusch_pagan(lm_pre_alpha) # Breusch-Pagan test

# - Resolving Heteroskedasticity using heteroskedasticity-consistent (HC) variance covariance matrix

# Compare models
stargazer(lm_pre_alpha,
          coeftest(lm_pre_alpha, vcov = vcovHC(lm_pre_alpha, method = "White2", type = "HC0")),
          coeftest(lm_pre_alpha, vcov = vcovHC(lm_pre_alpha, method = "White2", type = "HC1")),
          type = "text")


```

<br>

#### 1.2 Accounting for Interactions

**Note:** Advisor suggested not to inlude interaction terms except for specific testing.
```{r eval=FALSE, include=FALSE}
#data_binary_test <- data_binary[1:20]
#data_binary_test$sold_price <- data_binary$sold_price

#data_binary_test <- data_binary[1:15]

#lm_check_binary <- lm(data_binary$sold_price ~ ., data = data_binary_test)

#(start.time <- Sys.time())
#lm_check_binary_interactions <- lm(data_binary$sold_price ~ .^2, data = data_binary_test)
#(end.time <- Sys.time())
#(time.taken <- end.time - start.time)

#options(max.print=1000000)
#summary(lm_check)

#94^2 # Number of interactions checked

# Isolating only the interaction which are statistically significant

# 1. Create Boolean vector
#toselect_x <- summary(lm_check_binary_interactions)$coeff[-1,4] < 0.1

# 2. select sig. variables
#relevant_x <- names(toselect_x)[toselect_x == TRUE]
# PROBLEM: interaction are being name with a '1' at the end and that is fucking up the indexing for the last equation.
#(relevant_x <- sub("1", "", relevant_x))

# 3. formula with only sig. variables
#(sig_formula <- as.formula(paste("data_binary$sold_price ~",paste(relevant_x, collapse = "+"))))

# sig_model <- lm(formula = sig_formula, data_binary_test)
# summary(sig_model)

# Compare models
#summ(lm_check_binary, robust = "HC1")
#summ(lm_check_binary_interactions, robust = "HC1")
#summ(sig_model, robust = "HC1")
#stargazer(coeftest(lm_check, vcov = vcovHC(lm_check, method = "White2", type = "HC1")),
#          coeftest(lm_check_binary_interactions, vcov = vcovHC(lm_check_binary_interactions, method = "White2", type = "HC1")),
#          coeftest(sig_model, vcov = vcovHC(sig_model, method = "White2", type = "HC1")),
#         type = "text")
```

<br>

#### 1.3 Accounting for Non-linearity

##### 1.3.1 Age
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# Age
a <- ggplot(data_factor, aes(x = age , y = sold_price)) + 
    geom_point(aes(color = infections_period), alpha = 0.15) + 
    geom_smooth(aes(color = infections_period)) +
    geom_smooth(color = "grey50", linetype = "dashed") +
    theme_minimal()

# Actual vs. fit

# Model with non-linear addition
lm_pre_alpha_age <- lm(sold_price ~ . + I(age^2), data = data_factor_core)
summ(lm_pre_alpha_age)

# Marginal effects data frames
ggpredict_1 <- ggpredict(lm_pre_alpha, terms = "age")
ggpredict_2 <- ggpredict(lm_pre_alpha_age, terms = "age")

# Plots
b <- ggplot(data_factor_core, aes( x = age)) +
   geom_smooth(data_factor_core, mapping = aes(y = sold_price), color = "grey50") +
   geom_smooth(ggpredict_1, mapping = aes(x, predicted), linetype = "dashed", color = "darkred") +
   geom_smooth(ggpredict_2, mapping = aes(x, predicted), linetype = "dashed", color = "darkblue")

# Look at age & age^2 alone to see impact on more relevant y-axis scale
c <- ggplot() +
   geom_smooth(ggpredict_1, mapping = aes(x, predicted), linetype = "dashed", color = "darkred") +
   geom_smooth(ggpredict_2, mapping = aes(x, predicted), linetype = "dashed", color = "darkblue") 

a
gridExtra::grid.arrange(b,c, nrow =2, ncol = 1)

```

<br>

##### 1.3.2 Living Area
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# Living Area

# General graphing
ggplot(data_factor, aes(x = area_living , y = sold_price)) + 
    geom_point(aes(color = infections_period), alpha = 0.15) + 
    geom_smooth(aes(color = infections_period)) +
    geom_smooth(color = "grey50", linetype = "dashed") +
    theme_minimal()

ggplot(data_factor, aes(x = area_living , y = sold_price/area_living)) + 
    geom_point(aes(color = infections_period), alpha = 0.15) + 
    geom_smooth(aes(color = infections_period)) +
    geom_smooth(color = "grey50", linetype = "dashed") +
    theme_minimal()

# Actual vs. fit
# Model with non-linear addition
lm_pre_alpha_area <- lm(sold_price ~ . + I(area_living^2), data = data_factor_core)
summ(lm_pre_alpha_area)

# Model with single-variable fit
lm_pre_alpha_area_single <- lm(sold_price ~ area_living, data = data_factor_core)
summ(lm_pre_alpha_area_single)

# Marginal effects data frames
ggpredict_1 <- ggpredict(lm_pre_alpha, terms = "area_living") # total model
ggpredict_2 <- ggpredict(lm_pre_alpha_area, terms = "area_living") # non-linear addition
ggpredict_3 <- ggpredict(lm_pre_alpha_area_single, terms = "area_living") # single-variable fit

# Plots
ggplot(data_factor_core, aes(x = area_living)) +
   geom_smooth(data_factor, mapping = aes(y = sold_price), color = "grey50") +
   geom_smooth(ggpredict_1, mapping = aes(x, predicted), linetype = "dashed", color = "darkred") +
   geom_smooth(ggpredict_2, mapping = aes(x, predicted), linetype = "dashed", color = "darkblue") +
   geom_smooth(ggpredict_3, mapping = aes(x, predicted), linetype = "dashed", color = "darkblue")

# Look at age & age^2 alone to see impact on more relevant y-axis scale
ggplot() +
   geom_smooth(ggpredict_1, mapping = aes(x, predicted), linetype = "dashed", color = "darkred") +
   geom_smooth(ggpredict_2, mapping = aes(x, predicted), linetype = "dashed", color = "darkblue") 

# Conclusion


```

<br>

##### 1.3.3 Land
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# General graphing
ggplot(data_factor, aes(x = land_acres , y = sold_price)) + 
    geom_point(aes(color = infections_period), alpha = 0.15) + 
    geom_smooth(aes(color = infections_period)) +
    geom_smooth(color = "grey50", linetype = "dashed") +
    theme_minimal()

ggplot(data_factor, aes(x = land_acres, y = sold_price/land_acres)) + 
    geom_point(aes(color = infections_period), alpha = 0.15) + 
    geom_smooth(aes(color = infections_period)) +
    geom_smooth(color = "grey50", linetype = "dashed") +
    theme_minimal()
```

<br>

##### 1.3.4 Non-linear Additions

```{r, echo=TRUE}
#Additions
data_factor_core_clean <- data_factor_core
data_factor_core_clean$age_2 <- I(data_factor_core$age^2)
data_factor_core_clean$area_living_2 <- I(data_factor_core$area_living^2)
```

<br>

#### 1.4 Accounting for Multicollinearity
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# Full model summary
summ(lm_pre_alpha)

# Check Variance Inflation Factors (VIF)
VIF(lm_pre_alpha)
alias(lm_pre_alpha)

# Total area and living area are found to be significantly (i.e. VIF > 5) multicolinear (expected)
# Solution: Remove area_total

# Note the significant drop in R^2 from 0.99 to 0.86
lm_pre_alpha_cleaned <- lm(log(sold_price) ~ . - area_total ,data = data_factor_core)
summ(lm_pre_alpha_cleaned)
VIF(lm_pre_alpha_cleaned)

# Final pre_alpha
VIF(lm_pre_alpha_cleaned)
alias(lm_pre_alpha_cleaned)

# Another way to check for multicollinearity is visually through the mcvis package
data_numeric <- select_if(data_factor_core, is.numeric) # Subset numeric columns with dplyr
mcvis_result <- mcvis(X = data_numeric)
a <- plot(mcvis_result)

par(mfrow = c(2,2))
#Removals
data_numeric <- subset(data_numeric, select = -c(list_price))
mcvis_result <- mcvis(X = data_numeric)
b <- plot(mcvis_result)

#Removals
data_numeric <- subset(data_numeric, select = -c(area_total))
mcvis_result <- mcvis(X = data_numeric)
c <- plot(mcvis_result)

a
b
c



```

```{r, echo=FALSE,out.width="49%", out.height="20%",fig.cap="caption",fig.show='hold',fig.align='center'}

install.packages("cowplot")
install.packages("magick")
library(magick)
library(cowplot)
library(ggplot2)

p1 <- ggdraw() + draw_image("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Writing & Literature/Graphics from pptx/Multi_colin/multi_co1.png", scale = 1)
p2 <- ggdraw() + draw_image("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Writing & Literature/Graphics from pptx/Multi_colin/Multi_co2.png", scale = 1)

p3 <- ggdraw() + draw_image("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Writing & Literature/Graphics from pptx/Multi_colin/Multi_co3.png", scale = 1)

plot_grid(p1, p2, p3)
``` 

<br>

##### 1.4.1 Multicollinearity Removals
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# Removals
# - Area_total
# - Listing price

par(mfrow = (2,2))

data_factor_core_clean <- subset(data_factor_core_clean, select = -c(area_total, list_price))
```

<br>

### 1.5 Alpha Model
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}

# Finalized base model
lm_alpha <- lm(sold_price ~ . ,data = data_factor_core_clean)


summ(lm_alpha)
coeftest(lm_alpha, vcov = vcovHC(lm_alpha, method = "White2", type = "HC0"))
```

<br>

#### 2. Factor Analysis

##### 2.1 Corona
###### 2.1.1 Visualization
```{r, attr.output='style="max-height: 250px;"'}

# Waves of infection
ggplot(data_factor, aes(x = as.Date(sold_date), y = infections_3mma)) + 
    geom_point(color = "grey35") + 
    geom_smooth(linetype = "dashed", color = "gray46") +
    theme_minimal() +
    scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    scale_y_continuous(limits = c(0,max(infections_3mma))) +
    xlab(" ") +
    ylab("Confirmed Infections per Day") +
    labs(title = "Waves of Infection",
         caption = "") +
    geom_vline(xintercept = as.numeric(as.Date("2020-03-23")), linetype=4)

# Accumulation of infections
ggplot(data_factor, aes(x = as.Date(sold_date), y = I(infections_accum/1000))) + 
    geom_point(color = "grey35") + 
    geom_smooth(linetype = "dashed", color = "gray46") +
    theme_minimal() +
    scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    scale_y_continuous(limits = c(0,max(I(infections_accum/1000)))) +
    xlab(" ") +
    ylab("Accumulation of Infections (in 000's") +
    labs(title = "Accumulation of Infections",
         caption = "")

# Infections and home prices
ggplot(data_factor, aes(x = I(infections_3mma/1000), y = sold_price)) + 
    #geom_point() + 
    geom_smooth(linetype = "dashed", color = "gray46") +
    theme_minimal() +
    scale_x_continuous( limits = c(0,max(I(infections_3mma/1000)))) +
    xlab("3-Month Moving Average of Daily Infections (in 000's)") +
    ylab("Sold Price (Actual)") +
    labs(title = "Infections and Price",
         caption = "")

#Price on Infections
very_low <- "#460f5c"
low <- "#2c728e"
med <- "#27ad81"
high <- "#f4e61e"

# "#ff6c67", "#00c2c6"

ggplot(data_factor, aes(x = infections_period, y = sold_price, fill = infections_period)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)) +
    ggtitle("Comparison of Sold Price") +
    xlab("Infections Present (1 = yes)") +
    scale_fill_manual(values=c(ver, med))
```

<br><br>

###### 2.1.2 Modeling
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# Testing Corona
lm_corona <- lm(sold_price ~ infections_3mma + . 
                
                ,data = data_factor_core_clean)

summ(lm_corona)
coeftest(lm_corona, vcov = vcovHC(lm_corona, method = "White2", type = "HC0"))

# Visualizing marginal effect per positive tests on price
lm_corona_single <- lm(sold_price ~ infections_3mma 
                
                ,data = data_factor_core_clean)
summ(lm_corona_single)    

ggpredict_1 <- ggpredict(lm_corona, terms = "infections_3mma")
ggpredict_2 <- ggpredict(lm_corona_single, terms = "infections_3mma")

# Plots
ggplot(data_factor_core, aes(x = infections_3mma)) +
   geom_smooth(data_factor_core, mapping = aes(y = sold_price), color = "grey50") + # Actual Data
   geom_smooth(ggpredict_1, mapping = aes(x, predicted), linetype = "dashed", color = "darkred") + # Controlled model
   geom_smooth(ggpredict_2, mapping = aes(x, predicted), linetype = "dashed", color = "darkblue") + # Best single fit
   ggtitle("Model Fit Overview") 
 
# Predicting infections with house prices
lm_flip <- lm_flip <- lm(infections_3mma ~ sold_price , data = data_factor)
summ(lm_flip)

ggpredict_flip <- ggpredict(lm_flip, terms = "sold_price")

ggplot(data_factor, aes(x = sold_price)) +
   geom_smooth(data_factor, mapping = aes(y = infections_3mma), color = "grey50") +
   geom_smooth(ggpredict_flip, mapping = aes(x, predicted), linetype = "dashed", color = "darkred") +
   labs(title = "Flipped Regression", subtitle = "Explining Infections using Variations in Price",
         caption = "") 

```

<br>

##### 2.2 Corona on Number of Bedrooms

###### 2.2.1 Visualiztion
```{r, warning=FALSE}

# Distribution
# Find the mean of each group
library(plyr)
data_factor$beds_total <- as.numeric(data_factor$beds_total)
room_mean <- ddply(data_factor, "infections_period", summarise, beds_mean=mean(beds_total, na.rm = TRUE))

data_factor$beds_total <- as.numeric(data_factor$beds_total)
a <- ggplot(data_factor, aes(x=beds_total, fill = infections_period)) +
    geom_density(alpha = 0.5, position = "identity") +
    scale_fill_manual(values=c(very_low, med)) +
    labs(title = "Distibution of Number of Bedrooms") +
    geom_vline(data=room_mean, aes(xintercept = room_mean[2,2]), linetype="dashed", size= 0.4, color = very_low, alpha = 0.5) +
    geom_vline(data=room_mean, aes(xintercept = room_mean[1,2]), linetype="dashed", size= 0.4, alpha = 0.5) +
    xlab("Number of Bedrooms") +
    ylab("Density") +
    labs(fill = "Infection Period")


# Distribution of total price and number of beds
data_factor$beds_total <- as.factor(data_factor$beds_total)
b <- ggplot(data = subset(data_factor, !is.na(beds_total)), aes(x = beds_total, y = sold_price, fill = beds_total)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    #coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)) +
      labs(title = "Distributions of Sold Price by Number of Bedrooms",
         caption = "") +
      xlab("Number of Bedrooms") +
      ylab("Sold Price")

      #+
      #scale_fill_manual(values = c(very_low, med), 
      #                name = "Infection Period",
      #                labels = c("Pre", "Post"))

# Distribution of price and number of beds before and after corona period
c <- ggplot(data = subset(data_factor, !is.na(beds_total)), aes(x = beds_total, y = sold_price, fill = beds_total)) +
    geom_violin(data = subset(data_factor, !is.na(beds_total)), mapping = aes(alpha = 0.5, fill = infections_period)) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    #coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)) +
      labs(title = "Distributions of Sold Price by Number of Bedrooms", 
           subtitle = "Price Pre vs. Post Infection Period",
           caption = "") +
      xlab("Number of Bedrooms")  +
      ylab("Sold Price")

# Distribution of price per sqft. and number of beds
data_factor$beds_total <- as.factor(data_factor$beds_total)
d <- ggplot(data = subset(data_factor, !is.na(beds_total)), aes(x = beds_total, y = sold_price/area_living, fill = beds_total)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    #coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)) +
      labs( title = "Distributions of Sold Price by Number of Bedrooms", subtitle = "Sold Price Per Sqft.",
         caption = "") +
      xlab("Number of Bedrooms") +
      ylab("Sold Price per Sqft.")
  

# Distribution of price per sqft. and number of beds before and after corona period
e <- ggplot(data = subset(data_factor, !is.na(beds_total)), aes(x = beds_total, y = sold_price/area_living , fill = beds_total)) +
    geom_violin(data = subset(data_factor, !is.na(beds_total)), mapping = aes(alpha = 0.5, fill = infections_period)) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    #coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)) +
      labs( title = "Distributions of Sold Price by Number of Bedrooms", subtitle = "Sold Price Per Sqft. Pre vs. Post Infection Period",
         caption = "") +
      xlab("Number of Bedrooms")  +
      ylab("Sold Price per Sqft.")

gridExtra::grid.arrange(a)
gridExtra::grid.arrange(b)
gridExtra::grid.arrange(c)
gridExtra::grid.arrange(d)
gridExtra::grid.arrange(e)
#gridExtra::grid.arrange(b,c, ncol = 2)


```

###### 2.2.2 Modeling

Ideas

* Break into each room number

```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# Note on bedroom's relationship with all other size-related features:
#  - The interpretation of the coefficient is dependent on the other fixed size features, especially area_living. In the case that total area is fixed, the interpretation of this coefficient become the effect of more bedrooms for a fixed size. No one wants a 500 sqft. house with 8 bedrooms.  
#  - For this reason, when analyzing changes in bedrooms, total size is excluded

# Change data structure to factor
data_factor_core_clean$beds_total <- as.factor(data_factor_core_clean$beds_total)

# Single Model: Factor
lm_corona_bedrooms_single <- lm(sold_price ~ + beds_total ,data = data_factor_core_clean)
summ(lm_corona_bedrooms_single)
coeftest(lm_corona_bedrooms_single, vcov = vcovHC(lm_corona_bedrooms_single, method = "White2", type = "HC0"))

# Basic Test: Few Controls
lm_corona_bedrooms_basic <- lm(sold_price ~ 
                      + data_factor$infections_3mma + beds_total + data_factor$infections_3mma*beds_total 

                       # Removals
                       - area_living
                       - area_living_2 
                       - bath_full
                       - bath_half
                       - land_acres
                       - sold_date
                       - garage
                       - property_type
                      
                            ,data = data_factor_core_clean)
summ(lm_corona_bedrooms_basic)
coeftest(lm_corona_bedrooms_basic, vcov = vcovHC(lm_corona_bedrooms_basic, method = "White2", type = "HC0"))

# General Model: Controlled
lm_corona_bedrooms <- lm(sold_price ~ . +
               
                       # test variable(s)                    
                       + data_factor$infections_3mma + beds_total + data_factor$infections_3mma*beds_total
                       
                       # Removals
                       - area_living
                       - area_living_2 
                       - bath_full
                       - bath_half
                       - land_acres
                       - sold_date
                       - garage
                       - property_type
                       
                       ,data = data_factor_core_clean)
summ(lm_corona_bedrooms)
coeftest(lm_corona_bedrooms, vcov = vcovHC(lm_corona_bedrooms, method = "White2", type = "HC0"))

```

<br>

##### 2.3 Corona on Price Quantiles

###### 2.3.1 Visualization
```{r}

# Find the mean of each group
library(plyr)
price_means <- ddply(data_factor, "infections_period", summarise, price_mean = mean(sold_price, na.rm = TRUE))

# Distribution: Total
ggplot(data_factor, aes(x = sold_price)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    ggtitle("Price Distribution") +
    geom_vline(data=price_means, aes(xintercept = mean(sold_price)), linetype="dashed", size= 0.4, color = very_low, alpha = 0.8) +
    xlab("Sold Price") +
    ylab("Density") 

# Distribution: Infection
ggplot(data_factor, aes(x = sold_price, fill = infections_period)) +
    geom_density(alpha = 0.5, position = "identity") +
    ggtitle("Price Distributions") +
    geom_vline(data=price_means, aes(xintercept = price_means[2,2]), linetype="dashed", size= 0.4, color = med, alpha = 0.8) +
    geom_vline(data = price_means, aes(xintercept = price_means[1,2]), linetype="dashed", size= 0.4, color = very_low, alpha = 0.8) +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post")) +
    xlab("Sold Price") +
    ylab("Density") +
    labs(fill = "Infection Period")

# Distribution: Top vs. Bottom
ggplot(data_factor) +
    geom_density(aes(x = sold_price, fill = infections_period), alpha = 0.5, position = "identity") + 
    facet_grid(vars(top25_sold_price, bottom25_sold_price), scales = "free") +
    ggtitle("Price Distributions") +
    scale_fill_manual(values=c(very_low, med)) +
    xlab("Sold Price") +
    labs(fill = "Infection Period") +
    ylab("Density") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))

#Price and Infections
ggplot(data_factor, aes(x = infections_period, y = sold_price, fill = infections_period)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)) +
    ggtitle("Comparison of Sold Price") +
    xlab("Infection Period") +
    scale_fill_manual(values=c(very_low, med)) +
    ylab("Sold Price") 

```

<br>

###### 2.3.2 Modeling
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}

# Testing Corona, top 25% in price ---------------------------------------------------------------------

# Single Var Test
lm_corona_price_top_single <- lm(sold_price ~ . 
               
                       # test variable(s)                    
                       + data_factor$top50_sold_price
                       
                       # Removals
                       
                       ,data = data_factor_core_clean)
summ(lm_corona_price_top_single)
coeftest(lm_corona_price_top_single, vcov = vcovHC(lm_corona_price_top_single, method = "White2", type = "HC0"))


# General Model: No Controls 
lm_corona_price_top_basic <- lm(sold_price ~ +
               
                       # test variable(s)                    
                       + data_factor$infections_3mma + data_factor$top50_sold_price 
                       + data_factor$infections_3mma*data_factor$top50_sold_price
                       
                       # Removals
                       
                        ,data = data_factor_core_clean)
summ(lm_corona_price_top_basic)
coeftest(lm_corona_price_top_basic, vcov = vcovHC(lm_corona_price_top_basic, method = "White2", type = "HC0"))

# General Model: With Controls 
lm_corona_price_top <- lm(sold_price ~ . +
               
                       # test variable(s)                    
                       + data_factor$infections_3mma + data_factor$top50_sold_price 
                       + data_factor$infections_3mma*data_factor$top50_sold_price
                       
                       # Removals
                       
                       ,data = data_factor_core_clean)
summ(lm_corona_price_top)
coeftest(lm_corona_price_top, vcov = vcovHC(lm_corona_price_top, method = "White2", type = "HC0"))

# Testing Corona, Bottom 25% in price ------------------------------------------------------------------

# Single Var Test
lm_corona_price_bottom_single <- lm(sold_price ~ +
               
                       # test variable(s)                    
                       + bottom25_sold_price
                       
                       # Removals
                       
                       ,data = data_factor_core_clean)
summ(lm_corona_price_bottom)
coeftest(lm_corona_price_bottom_single, vcov = vcovHC(lm_corona_price_bottom_single, method = "White2", type = "HC0"))

# General Model: No controls
lm_corona_price_bottom_basic <- lm(sold_price ~ +
               
                       # test variable(s)                    
                       + data_factor$infections_3mma + bottom25_sold_price +
                         data_factor$infections_3mma*bottom25_sold_price
                       
                       # Removals
                       
                       ,data = data_factor_core_clean)
summ(lm_corona_price_bottom_basic)
coeftest(lm_corona_price_bottom_basic, vcov = vcovHC(lm_corona_price_bottom_basic, method = "White2", type = "HC0"))

# General Model: With controls
lm_corona_price_bottom <- lm(sold_price ~ . +
                         
                       # test variable(s)                    
                       + data_factor$infections_3mma + bottom25_sold_price
                       + data_factor$infections_3mma*bottom25_sold_price
                         
                       # Removals
                       - sold_date
                         
                         ,data = data_factor_core_clean)
summ(lm_corona_price_bottom)
coeftest(lm_corona_price_bottom, vcov = vcovHC(lm_corona_price_bottom, method = "White2", type = "HC0"))

```

<br>

##### 2.4 Corona on Age Quantiles

###### 2.4.1 Visualization
```{r}
# Conditional Mean
library(plyr)
age_mean_data <- ddply(data_factor, "infections_period", summarise, age_mean = mean(age, na.rm = TRUE))

# Distribution: Total
ggplot(data_factor, aes(x = age)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    ggtitle("Age Distribution") +
    geom_vline(aes(xintercept = mean(age)), linetype="dashed", size= 0.4, alpha = 0.5, color = very_low) +
    xlab("Age of Property") +
    ylab("Density")


# Distribution: Infection
ggplot(data_factor, aes(x = age, fill = infections_period)) +
    geom_density(alpha = 0.5, position = "identity") +
    ggtitle("Age Distributions") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post")) +
    geom_vline(data = age_mean_data, aes(xintercept = age_mean_data[2,2]), linetype="dashed", size= 0.5, color = med, alpha = 0.8) +
    geom_vline(data = age_mean_data, aes(xintercept = age_mean_data[1,2]), linetype="dashed", size= 0.5, alpha = 0.8, color = very_low) +
    xlab("Age of Property") +
    ylab("Density")

?scale_fill_discrete()

# Distribution: Top vs. Bottom
ggplot(data_factor) +
    geom_density(aes(x = age, fill = infections_period), alpha = 0.5, position = "identity") + 
                     facet_grid(vars(top25_age, bottom25_age), scales = "free") +
                     ggtitle("Age Distributions") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post")) +
    labs(fill = "Infection Period") +
    xlab("Age of Property") +
    ylab("Density")

#Age on Infections
ggplot(data_factor, aes(x = infections_period, y = age, fill = infections_period)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)) +
    ggtitle("Comparison of Age") +
    xlab("Infection Period") +
    ylab("Age of Property") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))




```

###### 2.4.2 Modeling
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# Testing Corona, top 25% in age
lm_corona_age_top_single <- lm(sold_price ~
               
                        # test variable(s)                    
                        + top25_age
                       
                        # Removals
                        - age
                        - age_2
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_age_top_single, vcov = vcovHC(lm_corona_age_top_single, method = "White2", type = "HC0"))

lm_corona_age_top <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + data_factor$infections_3mma + top25_age + data_factor$infections_3mma*top25_age 
                       
                        # Removals
                        - age
                        - age_2
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_age_top, vcov = vcovHC(lm_corona_age_top, method = "White2", type = "HC0"))

# Testing Corona, bottom 25% in age
lm_corona_age_bottom_single <- lm(sold_price ~ 
               
                        # test variable(s)                    
                        + bottom25_age
                       
                        # Removals
                        - age
                        - age_2
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_age_bottom, vcov = vcovHC(lm_corona_age_bottom, method = "White2", type = "HC0"))

lm_corona_age_bottom <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + data_factor$infections_3mma + bottom25_age + data_factor$infections_3mma*bottom25_age 
                       
                        # Removals
                        - age
                        - age_2
                        - property_type
                        - area_living
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_age_bottom, vcov = vcovHC(lm_corona_age_bottom, method = "White2", type = "HC0"))

```

<br>

##### 2.5 Corona on Size Quantiles

###### 2.5.1 Visualization
```{r}
# Conditional Mean
library(plyr)
area_living_mean_data <- ddply(data_factor, "infections_period", summarise, area_living_mean = mean(area_living, na.rm = TRUE))

# Distribution: Total
ggplot(data_factor, aes(x = area_living)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    ggtitle("Living Area Distribution") +
    geom_vline(aes(xintercept = mean(area_living)), linetype="dashed", size= 0.4, alpha = 0.5, color = very_low) +
    xlab("Living Area") +
    ylab("Density")


# Distribution: Infection
ggplot(data_factor, aes(x = area_living, fill = infections_period)) +
    geom_density(alpha = 0.5, position = "identity") +
    ggtitle("Living Area Distributions") +
    geom_vline(data = area_living_mean_data, aes(xintercept = area_living_mean_data[2,2]), linetype="dashed", size= 0.5, color = med, alpha = 0.8) +
    geom_vline(data = area_living_mean_data, aes(xintercept = area_living_mean_data[1,2]), linetype="dashed", size= 0.5, alpha = 0.8, color = very_low) +
    xlab("Living Area") +
    ylab("Density") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))

# Distribution: Top vs. Bottom
ggplot(data_factor) +
    geom_density(aes(x = area_living, fill = infections_period), alpha = 0.5, position = "identity") + 
    facet_grid(vars(top25_area_living, bottom25_area_living), scales = "free") +
    ggtitle("Living Area Distributions") +
    xlab("Living Area") +
    ylab("Density") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))

#area_living on Infections
ggplot(data_factor, aes(x = infections_period, y = area_living, fill = infections_period)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)) +
    ggtitle("Comparison of Living Area") +
    xlab("Infection Period") +
    ylab("Living Area") +
    scale_fill_manual(values=c(very_low, med))




```

###### 2.5.2 Modeling
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# Testing Corona, top 25% in area_living
lm_corona_area_living_top_single <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + top25_area_living
                       
                        # Removals
                        - area_living
                        - area_living_2
                        - beds_total
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_area_living_top_single, vcov = vcovHC(lm_corona_area_living_top_single, method = "White2", type = "HC0"))

lm_corona_area_living_top <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + data_factor$infections_3mma + top25_area_living + data_factor$infections_3mma*top25_area_living 
                       
                        # Removals
                        - area_living
                        - area_living_2
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_area_living_top, vcov = vcovHC(lm_corona_area_living_top, method = "White2", type = "HC0"))

# Testing Corona, bottom 25% in area_living
lm_corona_area_living_bottom_single <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + bottom25_area_living
                       
                        # Removals
                        - area_living
                        - area_living_2
                        - beds_total
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_area_living_bottom_single, vcov = vcovHC(lm_corona_area_living_bottom_single, method = "White2", type = "HC0"))

lm_corona_area_living_bottom <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + data_factor$infections_3mma + bottom25_area_living + data_factor$infections_3mma*bottom25_area_living 
                       
                        # Removals
                        - area_living
                        - area_living_2
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_area_living_bottom, vcov = vcovHC(lm_corona_area_living_bottom, method = "White2", type = "HC0"))
```

<br>

##### 2.6 Corona on Days on Market

###### 2.6.1 Visualization
```{r}
# Conditional Mean
library(plyr)
dom_mean_data <- ddply(data_factor, "infections_period", summarise, dom_mean = mean(dom, na.rm = TRUE))

# Distribution: Just for City
ggplot(data_factor, aes(x = dom)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    ggtitle("Days on Market Distribution") +
    geom_vline(aes(xintercept = mean(dom)), linetype="dashed", size= 0.4, alpha = 0.5, color = very_low) +
    xlab("Days on Market") +
    ylab("Density")


# Distribution: Infection
ggplot(data_factor, aes(x = dom, fill = infections_period)) +
    geom_density(alpha = 0.5, position = "identity") +
    ggtitle("Days on Market Distributions") +
    geom_vline(data = dom_mean_data, aes(xintercept = dom_mean_data[2,2]), linetype="dashed", size= 0.5, color = med, alpha = 0.8) +
    geom_vline(data = dom_mean_data, aes(xintercept = dom_mean_data[1,2]), linetype="dashed", size= 0.5, alpha = 0.8, color = very_low) +
    xlab("Days on Market") +
    ylab("Density") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))

# Distribution: Top vs. Bottom
ggplot(data_factor) +
    geom_density(aes(x = dom, fill = infections_period), alpha = 0.5, position = "identity") + 
    facet_grid(vars(top25_dom, bottom25_dom), scales = "free") +
    ggtitle("Days on Market Distributions") +
    xlab("Days on Market") +
    ylab("Density") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))

#dom on Infections
ggplot(data_factor, aes(x = infections_period, y = dom, fill = infections_period)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    #coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)) +
    ggtitle("Comparison of Days on Market") +
    xlab("Infection Period") +
    ylab("Days on Market") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post"))

```
###### 2.6.2 Modeling
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}
# Testing Corona, top 25% in dom
lm_corona_dom_top_single <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + top25_dom
                       
                        # Removals
                        - dom
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_dom_top_single, vcov = vcovHC(lm_corona_dom_top_single, method = "White2", type = "HC0"))

lm_corona_dom_top <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + data_factor$infections_3mma + top25_dom + data_factor$infections_3mma*top25_dom 
                       
                        # Removals
                        - dom
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_dom_top, vcov = vcovHC(lm_corona_dom_top, method = "White2", type = "HC0"))

# Testing Corona, bottom 25% in dom
lm_corona_dom_bottom_single <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + bottom25_dom
                       
                        # Removals
                        - dom
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_dom_bottom_single, vcov = vcovHC(lm_corona_dom_bottom_single, method = "White2", type = "HC0"))

lm_corona_dom_bottom <- lm(sold_price ~ .
               
                        # test variable(s)                    
                        + data_factor$infections_3mma + bottom25_dom + data_factor$infections_3mma*bottom25_dom 
                       
                        # Removals
                        - dom
                        - property_type
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_dom_bottom, vcov = vcovHC(lm_corona_dom_bottom, method = "White2", type = "HC0"))

# top 25% is too tight!! means aren't different

# this means that the premium for being in the bottom percentile of dom decreased. This make's sense because this was no longer a result of increased quality but increased demand.
```

<br>

##### 2.7 Corona on City

###### 2.7.1 Visualization
```{r, alpha = false}
# Conditional Mean
library(plyr)
city_limits_mean_data <- ddply(subset(data_factor, data_factor$city_limits ==1), "infections_period", summarise, city_limits_mean = mean(sold_price, na.rm = TRUE))

# Distribution: Just City
ggplot(data = subset(data_factor, data_factor$city_limits ==1), aes(x = sold_price)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    ggtitle("Price Distribution of Properties in City Limits") +
    geom_vline(aes(xintercept = mean(city_limits)), linetype="dashed", size= 0.4, alpha = 0.5) +
    xlab("Sold Price") +
    ylab("Density")

# Distribution: Infection
ggplot(data = subset(data_factor, data_factor$city_limits ==1), aes(x = sold_price, fill = infections_period)) +
    geom_density(alpha = 0.5, position = "identity") +
    ggtitle("Price Distributions of Properties in City Limits") +
    geom_vline(data = city_limits_mean_data, aes(xintercept = city_limits_mean_data[2,2]), linetype="dashed", size= 0.5, color = med, alpha = 0.8) +
    geom_vline(data = city_limits_mean_data, aes(xintercept = city_limits_mean_data[1,2]), linetype="dashed", size= 0.5, alpha = 0.8, color = very_low) +
    scale_fill_manual(values = c(very_low, med), 
                      name = "Infection Period",
                      labels = c("Pre", "Post")) +
    xlab("Sold Price") +
    ylab("Density") 

#city_limits on Infections
ggplot(data_factor, aes(x = city_limits, y = sold_price, fill = infections_period)) +
    geom_violin(alpha = 0.5) +
    geom_boxplot(width=0.1, alpha = 0.9) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="D") +
    #coord_flip() +
    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=14)) +
    ggtitle("Comparison of Price: City Limts and Pre vs. Post Corona") +
    xlab("City Limits and Infection Period") +
    ylab("Sold Price") +
    scale_fill_manual(values = c(very_low, med), 
                      name = "City Limits",
                      labels = c("Pre", "Post"))

```

###### 2.7.2 Modeling
```{r, warning=FALSE, attr.output='style="max-height: 250px;"'}

# Testing Corona, City Limits
lm_corona_city <- lm(sold_price ~ . 
               
                       # test variable(s)                    
                       + data_factor$infections_3mma + data_factor$city_limits 
                       + data_factor$infections_3mma*data_factor$city_limits
                       
                       
                       ,data = data_factor_core_clean)
coeftest(lm_corona_city, vcov = vcovHC(lm_corona_city, method = "White2", type = "HC0"))
```

<br><br>

#### 3. Playground

<br>

##### 3.1 Index creation

```{r}

data_index <- read_excel("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Models/Data/New Data/Index_hardkey.xlsx")
attach(data_index)

data_index_fred <- read_excel("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Models/Data/New Data/Index_FRED.xls")
attach(data_index_fred)

data_index_gdp <- read_excel("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Models/Data/New Data/la_GDP.xls")
attach(data_index_gdp)

data_index_fred_1975_total <- read_excel("/Users/sawyerbenson/Documents/Master Thesis/Thesis_Github/Models/Data/New Data/Total_US_1975.xls")
attach(data_index_fred_1975_total)

# Index graphing
ggplot(data_index, aes(x = Date)) +
    geom_line(mapping = aes(y = lma_2m_index), color = "darkred") +
    geom_line(mapping = aes(y = lma_3m_index), color = "darkgreen") +
    geom_line(mapping = aes(y = lma_4m_index), color = "darkblue") +
    geom_line(mapping = aes(y = lma_5m_index), color = "grey45") +
    geom_vline(xintercept = as.numeric(as.Date("2020-03-23")), linetype=4, color = "green") +
    #scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    scale_y_continuous(limits = c(min(lma_2m_index),max(lma_2m_index))) +
    xlab(" ") +
    ylab("Weighted Average Price per Sqft.") +
    labs(title = "Louisiana Housing Index",
         caption = "") 

# FRED quarterly data
ggplot(data_index_fred, aes(x = date)) + 
    geom_line(aes(y = index_Q1_1980), color = "darkred") +
    theme_minimal() +
    geom_vline(xintercept = as.Date("2020-01-01"), linetype=4, color = "green") +
    #scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    scale_y_continuous(limits = c(min(index_Q1_1980),max(index_Q1_1980))) +
    xlab(" ") +
    ylab("Index Value") +
    labs(title = "Louisiana Housing Index: FRED St. Louis",
         caption = "") 

# La Real GDP data quarterly data
data_index_gdp <- subset(data_index_gdp, data_index_gdp$date >= as.Date("2011-07-01"))
ggplot(data_index_gdp, aes(x = date)) + 
    geom_line(aes(y = real_gdp_Index, color = "darkred"), linetype = "dashed", size = .5) +
    geom_line(aes(y = real_gdp_re_specific_index, color = "darkblue"), size = .5) +
    theme(legend.position = "bottom") +
    geom_vline(xintercept = as.Date("2020-01-01"), linetype = 4, color = "green") +
    #scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    #scale_y_continuous(limits = c(min(real_gdp_Index),max(real_gdp_Index))) +
    xlab(" ") +
    ylab("Index Value") +
    labs(title = "Louisiana GDP and Housing Index: FRED St. Louis",
         caption = "") +
         scale_color_discrete(name = "Infection Period",
                              labels = c("RE Index", "Aggrigate GDP Index"))

cor.test(real_gdp_Index, real_gdp_re_specific_index)

# TOTAL US Real GDP data quarterly data base 2011
ggplot(data_index_fred_total, aes(x = observation_date)) + 
    geom_line(aes(y = GDP, color = very_low), linetype = "dashed", size = .5) +
    geom_line(aes(y = all_re_index, color = med), size = .5) +
    theme(legend.position = "bottom" ) +
    geom_vline(xintercept = as.Date("2020-01-01"), linetype=4, color = "green") +
    #scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    #scale_y_continuous(limits = c(min(real_gdp_Index),max(real_gdp_Index))) +
    xlab(" ") +
    ylab("Index Value") +
    labs(title = "Total US GDP and Housing Index: FRED St. Louis",
         caption = "") +
         scale_color_discrete(name = "Infection Period",
                              labels = c("RE Index", "Aggrigate GDP Index"))
  
                    
# TOTAL US Real GDP data quarterly data base 1975

# Nominal
ggplot(data_index_fred_1975_total, aes(x = date)) + 
    geom_line(aes(y = gdp_pc_nom_index_1975 , color = very_low), linetype = "dashed", size = .5) +
    geom_line(aes(y = re_nom_index_1975, color = med), size = .5) +
    theme(legend.position = "bottom" ) +
    #geom_vline(xintercept = as.Date("2020-01-01"), linetype=4, color = "green") +
    #scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    #scale_y_continuous(limits = c(min(real_gdp_Index),max(real_gdp_Index))) +
    xlab(" ") +
    ylab("Index Value (1975 Q1 = 100)") +
    labs(title = "U.S. GDP and Housing Index",
         caption = "FRED, St. Louis") +
         scale_color_discrete(name = "",
                              labels = c("Nominal Housing Prices", "Nominal GDP Per-Capita"))

corr_nom_1975 <- cor(gdp_pc_nom_index_1975, re_nom_index_1975)


# Real
ggplot(data_index_fred_1975_total, aes(x = date)) + 
    geom_line(aes(y = gdp_pc_real_index_1975 , color = very_low), linetype = "dashed", size = .5) +
    geom_line(aes(y = re_real_index_1975, color = med), size = .5) +
    theme(legend.position = "bottom" ) +
    #geom_vline(xintercept = as.Date("2020-01-01"), linetype=4, color = "green") +
    #scale_x_date(limits = as.Date(c("2020-01-01", "2021-12-31"))) +
    #scale_y_continuous(limits = c(min(real_gdp_Index),max(real_gdp_Index))) +
    xlab(" ") +
    ylab("Index Value (1975 Q1 = 100)") +
    labs(title = "U.S. GDP and Housing Index",
         caption = "FRED, St. Louis") +
         scale_color_discrete(name = "",
                              labels = c("Real Housing Prices", "Real GDP Per-Capita"))

corr_real_1975 <- cor(gdp_pc_real_index_1975, re_real_index_1975)

corr_nom_1975
corr_real_1975


```

##### 3.2 Playing with Maps
```{r}
# packages
require(ggplot2)
install.packages("ggmap")
require(maps)
install.packages(Geoc)



#Basic Map
LA <- map_data("state", region="louisiana")
ggplot(LA, aes(x=long, y=lat))+geom_polygon()


# data
salesCalls <- data.frame(State=rep("louisiana",5), 
                             City=c("Baton Rouge","New Orleans", "Shreveport",       "Lafayette", "Mandeville"),
                             Calls=c(10,5,8,13,2))

salesCalls <- cbind(geocode(as.character(salesCalls$City)), salesCalls)



?cbind

ggplot(LA, aes(x=long, y=lat)) +
  geom_polygon() +
  coord_map() +
  geom_point(data=salesCalls, aes(x=lon, y=lat, size=Calls), color="orange")


```

##### 3.3 Reduction in Dimensionality

```{r}
library(boot) # K-fold
library(leaps) # Subset 
library(glmnet) #glmnet() is the main function in the glmnet package (must pass in an x matrix as well as a y vector)

# Set x-y definitions for glmnet package 
x <- model.matrix(sold_price ~ . ,data = data_factor_core_clean)[, -1]

y <- data_factor_core_clean$sold_price[1:24653] # Manually restricted due rows not matching with x 'x' for an unknown reason

# General grid
grid <- exp(seq(10, -65, length = 101)) #grid of values from exp(10) [null model] to exp(-15) [least squares]

#Lasso
set.seed(1)
cv.out <- cv.glmnet(x, y, alpha = 1, lambda = grid, nfolds = 10) #lasso
plot(cv.out)

# Base decision
bestlam <- cv.out$lambda.min; bestlam; log(bestlam)
out <- cv.out$glmnet.fit
lasso.coef <- predict(out, type = "coefficients", s = bestlam); lasso.coef; lasso.coef[lasso.coef != 0]
sum(abs(lasso.coef[1:31])) #l1 norm

# +1se decision
bestlam2 <- cv.out$lambda.1se; bestlam2; log(bestlam2)
lasso.coef2 <- predict(out, type = "coefficients", s = bestlam2); lasso.coef2; lasso.coef2[lasso.coef2 != 0]
sum(abs(lasso.coef2[2:31])) #l1 norm

```


##### 3.4 Basic 3D Graphing
```{r}
kd <- with(MASS::geyser, MASS::kde2d(sold_price, infections_3mma, n = 50))

fig <- plot_ly(x = kd$x, y = kd$y, z = kd$z) %>% add_surface()

fig
```

##### 3.4 Descriptive Stats

```{r}
# Correlation Matrix heatmap
# Get numeric variable

data_factor$bath_full < as.numeric(data_factor$bath_full)
num_vars <- data_factor %>% dplyr::select(where(is.numeric))
num_vars <- subset(num_vars, select = -c(top50_sold_price))

# Corr matrix
cormat <- round(cor(num_vars),2)
head(cormat)

melted_cormat <- melt(cormat)
head(melted_cormat)

ggplot(data = melted_cormat, aes(x=Var1, y=Var2, fill = value)) + 
   geom_tile() +
   scale_fill_gradient2(low = very_low, 
                        high = high, 
                        mid = med, 
                        midpoint = 0, 
                        limit = c(-1,1), 
                        space = "Lab", 
                        name="Correlation") +
   theme_minimal() + 
   theme(axis.text.x = element_text(angle = 45, vjust = 1, size = 10, hjust = 1, color = "#2E2E2E"),
         axis.text.y = element_text(angle = 0, vjust = 1, size = 10, hjust = 1, color = "#2E2E2E")) +
   coord_fixed() +
   labs(title = "Correlation Matrix",
        x = "",
        y = "")


```

```{r}
# Distribution: Total
a <- ggplot(data_factor, aes(x = sold_price/1000)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Sold Price") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

b <- ggplot(data_factor, aes(x = list_price/1000)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("List Price") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 


c <- ggplot(data_factor, aes(x = area_living)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Living Area") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

d <- ggplot(data_factor, aes(x = land_acres)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Land in Acres") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

e <- ggplot(data_factor, aes(x = area_total)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Total Area") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

f <- ggplot(data_factor, aes(x = age)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Age") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

g <- ggplot(data_factor, aes(x = dom)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("DOM") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

data_factor$sold_date <- as.Date(data_factor$sold_date)
str(data_factor)
h <- ggplot(data_factor, aes(x = sold_date)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Sold Date") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) +
    scale_x_date(date_labels = "%Y")

i <- ggplot(data = subset(data_factor, data_factor$infections_daily > 1), aes(x = infections_daily)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    xlab("Infections Daily") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

data_factor$beds_total <- as.numeric(data_factor$beds_total)
j <- ggplot(data_factor, aes(x=beds_total)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    scale_fill_manual(values=c(very_low)) +
    xlab("Number of Bedrooms") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

data_factor$bath_full <- as.numeric(data_factor$bath_full)
k <- ggplot(data_factor, aes(x=bath_full)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    scale_fill_manual(values=c(very_low)) +
    xlab("Number of Full Bathrooms") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

data_factor$bath_half <- as.numeric(data_factor$bath_half)
l <- ggplot(data_factor, aes(x=bath_half)) +
    geom_density(alpha = 0.5, position = "identity", fill = very_low) +
    scale_fill_manual(values=c(very_low)) +
    xlab("Number of Half Bathrooms") +
    ylab("") + 
    theme(axis.text.y=element_blank(), 
          axis.ticks.y=element_blank(),
          text = element_text(size=10)) 

gridExtra::grid.arrange(a,b,c,d,e,f,g,h,i,j,k,l, nrow =4, ncol = 3)


```





end of document
>>>>>>> cd3d4497875c198536d12af185cf61114c92970a